def test_spike_sync_empty():
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_allclose(d, 1.0)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 1.0])
assert_array_equal(prof.y, [1.0, 1.0])
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([
0.4,
], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_allclose(d, 0.0)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 0.4, 1.0])
assert_array_equal(prof.y, [0.0, 0.0, 0.0])
st1 = SpikeTrain([
0.6,
], edges=(0.0, 1.0))
st2 = SpikeTrain([
0.4,
], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_almost_equal(d, 1.0, decimal=15)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [1.0, 1.0, 1.0, 1.0], decimal=15)
st1 = SpikeTrain([
0.2,
], edges=(0.0, 1.0))
st2 = SpikeTrain([
0.8,
], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_almost_equal(d, 0.0, decimal=15)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.2, 0.8, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [0.0, 0.0, 0.0, 0.0], decimal=15)
# test with empty intervals
st1 = SpikeTrain([2.0, 5.0], [0, 10.0])
st2 = SpikeTrain([2.1, 7.0], [0, 10.0])
st3 = SpikeTrain([5.1, 6.0], [0, 10.0])
res = spk.spike_sync_profile(st1, st2).avrg(interval=[3.0, 4.0])
assert_allclose(res, 1.0)
res = spk.spike_sync(st1, st2, interval=[3.0, 4.0])
assert_allclose(res, 1.0)
sync_matrix = spk.spike_sync_matrix([st1, st2, st3], interval=[3.0, 4.0])
assert_array_equal(sync_matrix, np.ones((3, 3)) - np.diag(np.ones(3)))
def test_multi_spike_sync():
# some basic multivariate check
spikes1 = SpikeTrain(
[100, 300, 400, 405, 410, 500, 700, 800, 805, 810, 815, 900], 1000)
spikes2 = SpikeTrain(
[100, 200, 205, 210, 295, 350, 400, 510, 600, 605, 700, 910], 1000)
spikes3 = SpikeTrain(
[100, 180, 198, 295, 412, 420, 510, 640, 695, 795, 820, 920], 1000)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes1, spikes3), 0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes2, spikes3), 0.5, decimal=15)
f = spk.spike_sync_profile_multi([spikes1, spikes2, spikes3])
# hands on definition of the average multivariate spike synchronization
# expected = (f1.integral() + f2.integral() + f3.integral()) / \
# (np.sum(f1.mp[1:-1])+np.sum(f2.mp[1:-1])+np.sum(f3.mp[1:-1]))
expected = 0.5
assert_almost_equal(f.avrg(), expected, decimal=15)
assert_almost_equal(spk.spike_sync_multi([spikes1, spikes2, spikes3]),
expected,
decimal=15)
# multivariate regression test
spike_trains = spk.load_spike_trains_from_txt(os.path.join(
TEST_PATH, "SPIKE_Sync_Test.txt"),
edges=[0, 4000])
# extract all spike times
spike_times = np.array([])
for st in spike_trains:
spike_times = np.append(spike_times, st.spikes)
spike_times = np.unique(np.sort(spike_times))
f = spk.spike_sync_profile_multi(spike_trains)
assert_equal(spike_times, f.x[1:-1])
assert_equal(len(f.x), len(f.y))
assert_equal(np.sum(f.y[1:-1]), 39932)
assert_equal(np.sum(f.mp[1:-1]), 85554)
# example with 2 empty spike trains
sts = []
sts.append(SpikeTrain([1, 9], [0, 10]))
sts.append(SpikeTrain([1, 3], [0, 10]))
sts.append(SpikeTrain([], [0, 10]))
sts.append(SpikeTrain([], [0, 10]))
assert_almost_equal(spk.spike_sync_multi(sts), 1.0 / 6.0, decimal=15)
assert_almost_equal(spk.spike_sync_profile_multi(sts).avrg(),
1.0 / 6.0,
decimal=15)
def test_regression_spiky():
# standard example
st1 = SpikeTrain(np.arange(100, 1201, 100), 1300)
st2 = SpikeTrain(np.arange(100, 1201, 110), 1300)
isi_dist = spk.isi_distance(st1, st2)
assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15)
isi_profile = spk.isi_profile(st1, st2)
assert_equal(isi_profile.y, 0.1/1.1 * np.ones_like(isi_profile.y))
spike_dist = spk.spike_distance(st1, st2)
assert_equal(spike_dist, 2.1105878248735391e-01)
spike_sync = spk.spike_sync(st1, st2)
assert_equal(spike_sync, 8.6956521739130432e-01)
# multivariate check
spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt",
(0.0, 4000.0))
isi_dist = spk.isi_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15)
spike_profile = spk.spike_profile_multi(spike_trains)
assert_equal(len(spike_profile.y1)+len(spike_profile.y2), 1252)
spike_dist = spk.spike_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(spike_dist, 2.4432433330596512e-01, decimal=15)
spike_sync = spk.spike_sync_multi(spike_trains)
# get the full precision from SPIKY
assert_equal(spike_sync, 0.7183531505298066)
def Synchro(spikes1, spikes2):
synchs = []
for i in range(len(spikes1)):
sp1 = pyspike.SpikeTrain(np.where(spikes1[i] > 0)[0], len(spikes1[i]))
sp2 = pyspike.SpikeTrain(np.where(spikes2[i] > 0)[0], len(spikes2[i]))
synchs.append(pyspike.spike_sync(sp1, sp2))
return np.array(synchs)
def test_regression_spiky():
# standard example
st1 = SpikeTrain(np.arange(100, 1201, 100), 1300)
st2 = SpikeTrain(np.arange(100, 1201, 110), 1300)
isi_dist = spk.isi_distance(st1, st2)
assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15)
isi_profile = spk.isi_profile(st1, st2)
assert_equal(isi_profile.y, 0.1 / 1.1 * np.ones_like(isi_profile.y))
spike_dist = spk.spike_distance(st1, st2)
assert_equal(spike_dist, 0.211058782487353908)
spike_sync = spk.spike_sync(st1, st2)
assert_equal(spike_sync, 8.6956521739130432e-01)
# multivariate check
spike_trains = spk.load_spike_trains_from_txt(
os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0))
isi_dist = spk.isi_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15)
spike_profile = spk.spike_profile_multi(spike_trains)
assert_equal(len(spike_profile.y1) + len(spike_profile.y2), 1252)
spike_dist = spk.spike_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(spike_dist, 0.25188056475463755, decimal=15)
spike_sync = spk.spike_sync_multi(spike_trains)
# get the full precision from SPIKY
assert_equal(spike_sync, 0.7183531505298066)
# Eero's edge correction example
st1 = SpikeTrain([0.5, 1.5, 2.5], 6.0)
st2 = SpikeTrain([3.5, 4.5, 5.5], 6.0)
f = spk.spike_profile(st1, st2)
expected_times = np.array([0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.0])
y_all = np.array([
0.271604938271605, 0.271604938271605, 0.271604938271605,
0.617283950617284, 0.617283950617284, 0.444444444444444,
0.285714285714286, 0.285714285714286, 0.444444444444444,
0.617283950617284, 0.617283950617284, 0.271604938271605,
0.271604938271605, 0.271604938271605
])
expected_y1 = y_all[::2]
expected_y2 = y_all[1::2]
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y1, expected_y1, decimal=14)
assert_array_almost_equal(f.y2, expected_y2, decimal=14)
def test_spike_sync():
spikes1 = SpikeTrain([1.0, 2.0, 3.0], 4.0)
spikes2 = SpikeTrain([2.1], 4.0)
expected_x = np.array([0.0, 1.0, 2.0, 2.1, 3.0, 4.0])
expected_y = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
# test with some small max_tau, spike_sync should be 0
assert_almost_equal(spk.spike_sync(spikes1, spikes2, max_tau=0.05),
0.0, decimal=16)
spikes2 = SpikeTrain([3.1], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = SpikeTrain([1.1], 4.0)
expected_x = np.array([0.0, 1.0, 1.1, 2.0, 3.0, 4.0])
expected_y = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = SpikeTrain([0.9], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = SpikeTrain([3.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = SpikeTrain([1.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = SpikeTrain([1.5, 3.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.4, decimal=16)
def spike_analysis(spikes, value):
distances = []
i = 0
for spike in spikes:
spike_train = SpikeTrain(spike, [0.0, 300.0])
isi_profile = spk.spike_sync(prototype_trains[i], spike_train)
distances.append(isi_profile)
i += 1
val, idx = max((val, idx) for (idx, val) in enumerate(distances))
print("Distance: %.8f" % val)
print("Index: %s" % idx)
def test_multi_spike_sync():
# some basic multivariate check
spikes1 = SpikeTrain([100, 300, 400, 405, 410, 500, 700, 800,
805, 810, 815, 900], 1000)
spikes2 = SpikeTrain([100, 200, 205, 210, 295, 350, 400, 510,
600, 605, 700, 910], 1000)
spikes3 = SpikeTrain([100, 180, 198, 295, 412, 420, 510, 640,
695, 795, 820, 920], 1000)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes1, spikes3),
0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes2, spikes3),
0.5, decimal=15)
f = spk.spike_sync_profile_multi([spikes1, spikes2, spikes3])
# hands on definition of the average multivariate spike synchronization
# expected = (f1.integral() + f2.integral() + f3.integral()) / \
# (np.sum(f1.mp[1:-1])+np.sum(f2.mp[1:-1])+np.sum(f3.mp[1:-1]))
expected = 0.5
assert_almost_equal(f.avrg(), expected, decimal=15)
assert_almost_equal(spk.spike_sync_multi([spikes1, spikes2, spikes3]),
expected, decimal=15)
# multivariate regression test
spike_trains = spk.load_spike_trains_from_txt("test/SPIKE_Sync_Test.txt",
edges=[0, 4000])
# extract all spike times
spike_times = np.array([])
for st in spike_trains:
spike_times = np.append(spike_times, st.spikes)
spike_times = np.unique(np.sort(spike_times))
f = spk.spike_sync_profile_multi(spike_trains)
assert_equal(spike_times, f.x[1:-1])
assert_equal(len(f.x), len(f.y))
assert_equal(np.sum(f.y[1:-1]), 39932)
assert_equal(np.sum(f.mp[1:-1]), 85554)
def calculate_spike_synchrony_pyspike(ts, gids, interval=None):
if interval is None:
tmin = np.min(ts)
tmax = np.max(ts)
interval = [tmin, tmax]
s = spike_trains_list_of_list(ts, gids)
spike_trains = []
for i in range(len(s)):
spike_trains.append(spk.SpikeTrain(s[i], interval, is_sorted=False))
spk_sync = spk.spike_sync(spike_trains, interval=interval, max_tau=0.1)
return spk_sync
def test_spike_sync_empty():
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_equal(d, 1.0)
prof = spk.spike_sync_profile(st1, st2)
assert_equal(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 1.0])
assert_array_equal(prof.y, [1.0, 1.0])
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_equal(d, 0.0)
prof = spk.spike_sync_profile(st1, st2)
assert_equal(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 0.4, 1.0])
assert_array_equal(prof.y, [0.0, 0.0, 0.0])
st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_almost_equal(d, 1.0, decimal=15)
prof = spk.spike_sync_profile(st1, st2)
assert_equal(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [1.0, 1.0, 1.0, 1.0], decimal=15)
st1 = SpikeTrain([0.2, ], edges=(0.0, 1.0))
st2 = SpikeTrain([0.8, ], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_almost_equal(d, 0.0, decimal=15)
prof = spk.spike_sync_profile(st1, st2)
assert_equal(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.2, 0.8, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [0.0, 0.0, 0.0, 0.0], decimal=15)
def test_spike_sync():
spikes1 = SpikeTrain([1.0, 2.0, 3.0], 4.0)
spikes2 = SpikeTrain([2.1], 4.0)
expected_x = np.array([0.0, 1.0, 2.0, 2.1, 3.0, 4.0])
expected_y = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
# test with some small max_tau, spike_sync should be 0
assert_almost_equal(spk.spike_sync(spikes1, spikes2, max_tau=0.05),
0.0,
decimal=16)
spikes2 = SpikeTrain([3.1], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([1.1], 4.0)
expected_x = np.array([0.0, 1.0, 1.1, 2.0, 3.0, 4.0])
expected_y = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([0.9], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([3.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([1.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([1.5, 3.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.4, decimal=16)
run(runTime*ms)
#code below calculates and stores the pyspike metrics
firingValuesWithUnits = Sp1.spike_trains().values()
firingValues = []
for i in range(len(firingValuesWithUnits)):
firingValues.append(array(firingValuesWithUnits[i]))
fV = open('fv.txt','w')
for item in firingValues:
item = (" ".join(map(str,item)))
fV.write("%s\n" % item)
fV.close()
spikeTrains = psp.load_spike_trains_from_txt("fv.txt",edges=(0,runTime/1000.0))
qvalues.iloc[currentLine,0] = tc
qvalues.iloc[currentLine,1] = delay
qvalues.iloc[currentLine,2] = psyn
qvalues.iloc[currentLine,3] = synw
qvalues.iloc[currentLine,4] = psp.spike_distance(spikeTrains)
qvalues.iloc[currentLine,5] = psp.isi_distance(spikeTrains)
qvalues.iloc[currentLine,6] = psp.spike_sync(spikeTrains)
currentLine += 1
del G1
del S1
del Sp1
del firingValuesWithUnits
del firingValues
del spikeTrains
qvalues.to_excel('qvalues.xlsx', sheet_name='Sheet1')
# spike synchronization profile
f = spk.spike_sync_profile(spike_trains[0], spike_trains[1])
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")
# The optional parameter averaging_window_size determines the size
# of an averaging window to smoothen the profile. If this value is
# 0, no averaging is performed.
print("Average:", f.avrg())
# For the direct computation of the overall spike synchronization
# value within some interval, the spike_sync() function can be used:
spike_sync = spk.spike_sync(spike_trains[0], spike_trains[1], interval=ival)
# Computes the peri-stimulus time histogram
# The PSTH is simply the histogram of merged spike events.
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("Number of spike trains: %d" % len(spike_trains))
# num_of_spikes = sum([len(spk) for spk in spike_trains])
# print("Number of spikes: %d" % num_of_spikes)
# spike_train_order.
# Generates a Poisson spike train with the given
# rate in the given time interval
st1 = spk.generate_poisson_spikes(1.0, [0, 20])
# Plot simple relationship within errors within an hourly window.
normal = pd.DataFrame(spk.spike_sync_matrix(spike_trains, max_tau=60 * 60),
index=labels,
columns=labels)
save_matrix_plot(normal, 'Priority errors, Norway',
'{}/Norway_errors.png'.format(sys.argv[2]))
# First simple experiment: shift every priority one hour. Somewhat arbirtrarily.
shifted = pd.DataFrame(index=labels, columns=labels)
for P in shifted.index:
P_shifted = df_to_spike_train(
fhs[fhs.priority == P].shift(-1, pd.Timedelta(hours=1)), t_start,
edges)
shifted[P] = [
spk.spike_sync(P_shifted, s_t, max_tau=60 * 60) for s_t in spike_trains
]
shifted.index = ['$\mathregular{' + x + '_{-1h}}$' for x in labels]
save_matrix_plot(shifted, 'Priority errors shifted, Norway',
'{}/Norway_errors_shifted.png'.format(sys.argv[2]))
P1 = fhs.query("priority == 'P1'")
P2 = fhs.query("priority == 'P2'")
# More complex experiment: find which time shift led to the best synchronous spike correlation.
results = pd.DataFrame(index=np.linspace(0, 60, 13, dtype=int),
columns=np.unique(fhs.county),
dtype=float)
for delay in results.index:
for county in results.columns:
def analyze_result(name, stim, result, fs=10000, save=True):
analysis = {}
spikes_e = result['spikes_e']
spikes_stim = result['spikes_stim']
spikes_i = result['spikes_i']
# Get Ns and ts
ns_e, ts_e = spikes_e.i, spikes_e.t / second
ns_i, ts_i = spikes_i.i, spikes_i.t / second
ns_stim, ts_stim = spikes_stim.i, spikes_stim.t / second
# Keep only neurons 0-199
mask = ns_e < 200
ns_e, ts_e = ns_e[mask], ts_e[mask]
mask = ns_i < 200
ns_i, ts_i = ns_i[mask], ts_i[mask]
# -------------------------------------------------------------
# Drop first 200 ms
mask = ts_e > 0.2
ns_e, ts_e = ns_e[mask], ts_e[mask]
mask = ts_i > 0.2
ns_i, ts_i = ns_i[mask], ts_i[mask]
# -------------------------------------------------------------
# Look at pre-stim first
mask = ts_e < stim
ns_pre_e, ts_pre_e = ns_e[mask], ts_e[mask]
mask = ts_i < stim
ns_pre_i, ts_pre_i = ns_i[mask], ts_i[mask]
# kappa
r_e = futil.kappa(ns_pre_e, ts_pre_e, ns_pre_e, ts_pre_e, (0, 1), 1.0 / 1000) # 1 ms bins
analysis['kappa_pre_e'] = r_e
r_i = futil.kappa(ns_pre_i, ts_pre_i, ns_pre_i, ts_pre_i, (0, 1), 1.0 / 1000) # 1 ms bins
analysis['kappa_pre_i'] = r_i
# fano
fanos_e = futil.fano(ns_pre_e, ts_pre_e)
mfano_e = np.nanmean([x for x in fanos_e.values()])
analysis['fano_pre_e'] = mfano_e
# -------------------------------------------------------------
# Drop times before stim time
mask = ts_e > stim
ns_e, ts_e = ns_e[mask], ts_e[mask]
mask = ts_i > stim
ns_i, ts_i = ns_i[mask], ts_i[mask]
# kappa
r_e = futil.kappa(ns_e, ts_e, ns_e, ts_e, (0, 1), 1.0 / 1000) # 1 ms bins
analysis['kappa_e'] = r_e
r_i = futil.kappa(ns_i, ts_i, ns_i, ts_i, (0, 1), 1.0 / 1000) # 1 ms bins
analysis['kappa_i'] = r_i
# fano
fanos_e = futil.fano(ns_e, ts_e)
mfano_e = np.nanmean([x for x in fanos_e.values()])
analysis['fano_e'] = mfano_e
# ISI and SPIKE
sto_e = spk.SpikeTrain(ts_e, (0.5, 1))
sto_stim = spk.SpikeTrain(ts_stim, (0.5, 1))
sto_e.sort()
sto_stim.sort()
isi = spk.isi_distance(sto_stim, sto_e)
sync = spk.spike_sync(sto_stim, sto_e)
analysis['isi_e'] = isi
analysis['sync_e'] = sync
# l distance and spike
ordered_e, _ = futil.ts_sort(ns_e, ts_e)
ordered_stim, _ = futil.ts_sort(ns_stim, ts_stim)
lev = futil.levenshtein(list(ordered_stim), list(ordered_e))
analysis['lev_e'] = lev
if save:
with open(name + '_analysis.csv', 'w') as f:
[f.write('{0},{1}\n'.format(k, v)) for k, v in analysis.items()]
return analysis
plt.savefig('fig/spike_sync_2.png')
plt.close()
# multivariant---------------------------------------------#
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.legend(loc='lower right')
f_direct = spk.spike_sync(spike_trains, interval=[0, 4000])
print "Average from spike_sync_profile :", f.avrg()
print "directly from spike_sync :", f_direct
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")
plt.legend()
plt.savefig('fig/spike_sync_multi.png')
plt.close()
# plt.show()
t_start = first - pd.Timedelta(seconds=60*60+1)
t_end = last + pd.Timedelta(seconds=1)
edges = (0, (t_end - t_start).total_seconds())
spike_trains = [ df_to_spike_train(fhs.query('priority == @priority'), t_start, edges) for priority in labels ]
# Plot simple relationship within errors within an hourly window.
normal = pd.DataFrame(spk.spike_sync_matrix(spike_trains, max_tau=60*60), index=labels, columns=labels)
save_matrix_plot(normal, 'Priority errors, Norway', '{}/Norway_errors.png'.format(sys.argv[2]))
# First simple experiment: shift every priority one hour. Somewhat arbirtrarily.
shifted = pd.DataFrame(index=labels, columns=labels)
for P in shifted.index:
P_shifted = df_to_spike_train(fhs[ fhs.priority == P ].shift(-1, pd.Timedelta(hours=1)), t_start, edges)
shifted[P] = [ spk.spike_sync(P_shifted, s_t, max_tau=60*60) for s_t in spike_trains ]
shifted.index = [ '$\mathregular{'+x+'_{-1h}}$' for x in labels ]
save_matrix_plot(shifted, 'Priority errors shifted, Norway', '{}/Norway_errors_shifted.png'.format(sys.argv[2]))
P1 = fhs.query("priority == 'P1'")
P2 = fhs.query("priority == 'P2'")
# More complex experiment: find which time shift led to the best synchronous spike correlation.
results = pd.DataFrame(index = np.linspace(0,60,13,dtype=int), columns = np.unique(fhs.county), dtype=float)
for delay in results.index:
for county in results.columns:
spike_trains = [ df_to_spike_train(fhs.query('county == @county and priority == @priority'), t_start, edges)
for priority in labels ]
P1_shift = df_to_spike_train(P1.query('county == @county').shift(-1, pd.Timedelta(minutes=delay)), t_start, edges)
def test_spike_sync():
spikes1 = SpikeTrain([1.0, 2.0, 3.0], 4.0)
spikes2 = SpikeTrain([2.1], 4.0)
expected_x = np.array([0.0, 1.0, 2.0, 2.1, 3.0, 4.0])
expected_y = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
# test with some small max_tau, spike_sync should be 0
assert_almost_equal(spk.spike_sync(spikes1, spikes2, max_tau=0.05),
0.0,
decimal=16)
spikes2 = SpikeTrain([3.1], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([1.1], 4.0)
expected_x = np.array([0.0, 1.0, 1.1, 2.0, 3.0, 4.0])
expected_y = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([0.9], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([3.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([1.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16)
spikes2 = SpikeTrain([1.5, 3.0], 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.4, decimal=16)
spikes1 = SpikeTrain([1.0, 2.0, 4.0], 4.0)
spikes2 = SpikeTrain([3.8], 4.0)
spikes3 = SpikeTrain([
3.9,
], 4.0)
expected_x = np.array([0.0, 1.0, 2.0, 3.8, 4.0, 4.0])
expected_y = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 1.0])
f = spk.spike_sync_profile(spikes1, spikes2)
assert_array_almost_equal(f.x, expected_x, decimal=16)
assert_array_almost_equal(f.y, expected_y, decimal=16)
f2 = spk.spike_sync_profile(spikes2, spikes3)
i1 = f.integral()
i2 = f2.integral()
f.add(f2)
i12 = f.integral()
assert_equal(i1[0] + i2[0], i12[0])
assert_equal(i1[1] + i2[1], i12[1])