def test_usage_of_spikes(self):
st = SpikeTrain([16.5 * math.pi, 17.5 * math.pi,
18.5 * math.pi, 19.5 * math.pi], units='ms', t_stop=20 * math.pi)
STA = sta.spike_triggered_average(
self.asiga0, st, (-math.pi * ms, math.pi * ms))
self.assertEqual(STA.annotations['used_spikes'], 3)
self.assertEqual(STA.annotations['unused_spikes'], 1)
def test_usage_of_spikes(self):
st = SpikeTrain([16.5 * math.pi, 17.5 * math.pi,
18.5 * math.pi, 19.5 * math.pi], units='ms', t_stop=20 * math.pi)
STA = sta.spike_triggered_average(
self.asiga0, st, (-math.pi * ms, math.pi * ms))
self.assertEqual(STA.annotations['used_spikes'], 3)
self.assertEqual(STA.annotations['unused_spikes'], 1)
def test_spike_triggered_average_with_shifted_sin_wave(self):
"""Signal should average to zero"""
STA = sta.spike_triggered_average(
self.asiga0, self.st0, (-4 * ms, 4 * ms))
target = 5e-2 * mV
self.assertEqual(np.abs(STA).max().dimensionality.simplified,
pq.Quantity(1, "V").dimensionality.simplified)
self.assertLess(np.abs(STA).max(), target)
def test_spike_triggered_average_with_shifted_sin_wave(self):
'''Signal should average to zero'''
STA = sta.spike_triggered_average(
self.asiga0, self.st0, (-4 * ms, 4 * ms))
target = 5e-2 * mV
self.assertEqual(np.abs(STA).max().dimensionality.simplified,
pq.Quantity(1, "V").dimensionality.simplified)
self.assertLess(np.abs(STA).max(), target)
def test_one_spiketrain_empty(self):
'''Test for one empty SpikeTrain, but existing spikes in other'''
st = [SpikeTrain(
[9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms', t_stop=self.asiga1.t_stop),
SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)]
STA = sta.spike_triggered_average(self.asiga1, st, (-1 * ms, 1 * ms))
cmp_array = AnalogSignal(np.array([np.zeros(20, dtype=float)]).T,
units='mV', sampling_rate=10 / ms)
cmp_array = cmp_array / 0.
cmp_array.t_start = -1 * ms
assert_array_equal(STA.magnitude[:, 1], cmp_array.magnitude[:, 0])
def test_one_spiketrain_empty(self):
'''Test for one empty SpikeTrain, but existing spikes in other'''
st = [SpikeTrain(
[9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms', t_stop=self.asiga1.t_stop),
SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)]
STA = sta.spike_triggered_average(self.asiga1, st, (-1 * ms, 1 * ms))
cmp_array = AnalogSignalArray(np.array([np.zeros(20, dtype=float)]).T,
units='mV', sampling_rate=10 / ms)
cmp_array = cmp_array / 0.
cmp_array.t_start = -1 * ms
assert_array_equal(STA[:, 1], cmp_array[:, 0])
def test_all_spiketrains_empty(self):
st = SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger warnings.
STA = sta.spike_triggered_average(
self.asiga1, st, (-1 * ms, 1 * ms))
self.assertEqual("No spike at all was either found or used "
"for averaging", str(w[-1].message))
nan_array = np.empty(20)
nan_array.fill(np.nan)
cmp_array = AnalogSignal(np.array([nan_array, nan_array]).T,
units='mV', sampling_rate=10 / ms)
assert_array_equal(STA.magnitude, cmp_array.magnitude)
def test_all_spiketrains_empty(self):
st = SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger warnings.
STA = sta.spike_triggered_average(
self.asiga1, st, (-1 * ms, 1 * ms))
self.assertEqual("No spike at all was either found or used "
"for averaging", str(w[-1].message))
nan_array = np.empty(20)
nan_array.fill(np.nan)
cmp_array = AnalogSignal(np.array([nan_array, nan_array]).T,
units='mV', sampling_rate=10 / ms)
assert_array_equal(STA.magnitude, cmp_array.magnitude)
def test_spike_triggered_average_with_n_spikes_on_constant_function(self):
"""Signal should average to the input"""
const = 13.8
x = const * np.ones(201)
asiga = AnalogSignal(
np.array([x]).T, units='mV', sampling_rate=10 / ms)
st = SpikeTrain([3, 5.6, 7, 7.1, 16, 16.3], units='ms', t_stop=20)
window_starttime = -2 * ms
window_endtime = 2 * ms
STA = sta.spike_triggered_average(
asiga, st, (window_starttime, window_endtime))
a = int(((window_endtime - window_starttime) *
asiga.sampling_rate).simplified)
cutout = asiga[0: a]
cutout.t_start = window_starttime
assert_array_almost_equal(STA, cutout, 12)
def test_one_spiketrain_empty(self):
"""Test for one empty SpikeTrain, but existing spikes in other"""
st = [SpikeTrain(
[9 * math.pi, 10 * math.pi, 11 * math.pi, 12 * math.pi],
units='ms', t_stop=self.asiga1.t_stop),
SpikeTrain([], units='ms', t_stop=self.asiga1.t_stop)]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
"""
Ignore the RuntimeWarning: invalid value encountered in true_divide
new_signal = f(other, *args) for the empty SpikeTrain.
"""
STA = sta.spike_triggered_average(self.asiga1,
spiketrains=st,
window=(-1 * ms, 1 * ms))
assert np.isnan(STA.magnitude[:, 1]).all()
def test_spike_triggered_average_with_n_spikes_on_constant_function(self):
'''Signal should average to the input'''
const = 13.8
x = const * np.ones(201)
asiga = AnalogSignal(
np.array([x]).T, units='mV', sampling_rate=10 / ms)
st = SpikeTrain([3, 5.6, 7, 7.1, 16, 16.3], units='ms', t_stop=20)
window_starttime = -2 * ms
window_endtime = 2 * ms
STA = sta.spike_triggered_average(
asiga, st, (window_starttime, window_endtime))
a = int(((window_endtime - window_starttime) *
asiga.sampling_rate).simplified)
cutout = asiga[0: a]
cutout.t_start = window_starttime
assert_array_almost_equal(STA, cutout, 12)
def test_only_one_spike(self):
"""The output should be the same as the input"""
x = np.arange(0, 20, 0.1)
y = x**2
sr = 10 / ms
z = AnalogSignal(np.array([y]).T, units='mV', sampling_rate=sr)
spiketime = 8 * ms
spiketime_in_ms = int((spiketime / ms).simplified)
st = SpikeTrain([spiketime_in_ms], units='ms', t_stop=20)
window_starttime = -3 * ms
window_endtime = 5 * ms
STA = sta.spike_triggered_average(
z, st, (window_starttime, window_endtime))
cutout = z[int(((spiketime + window_starttime) * sr).simplified):
int(((spiketime + window_endtime) * sr).simplified)]
cutout.t_start = window_starttime
assert_array_equal(STA, cutout)
def test_only_one_spike(self):
'''The output should be the same as the input'''
x = np.arange(0, 20, 0.1)
y = x**2
sr = 10 / ms
z = AnalogSignal(np.array([y]).T, units='mV', sampling_rate=sr)
spiketime = 8 * ms
spiketime_in_ms = int((spiketime / ms).simplified)
st = SpikeTrain([spiketime_in_ms], units='ms', t_stop=20)
window_starttime = -3 * ms
window_endtime = 5 * ms
STA = sta.spike_triggered_average(
z, st, (window_starttime, window_endtime))
cutout = z[int(((spiketime + window_starttime) * sr).simplified):
int(((spiketime + window_endtime) * sr).simplified)]
cutout.t_start = window_starttime
assert_array_equal(STA, cutout)
t_start=650.0*pq.ms)
wns_list.append(wns2)
#%% Set up STA excluding spikes in first 500 ms
os.chdir('/home/ngg1/[email protected]/Data_Urban/NEURON/Analysis/'
'AlmogAndKorngreen2014/ModCell_5_thrdsafe/figures')
for item in g_list:
plt.figure()
plt.xlabel('Time Before Spike (ms)')
plt.ylabel('Current (nA)')
plt.title(item+' Channel STA')
for key in channels[item]:
sta_ind = pd.DataFrame() #starting time column to build on
for i in range(50): #adding each columb of sta to df
sta_i = spike_triggered_average(wns_list[i],
channels[item][key]['neoSpkTrain500'][i],
(-40*pq.ms,5.1*pq.ms))
sta_i_df = pd.DataFrame(sta_i.as_array().flatten())
sta_ind = pd.concat([sta_ind,sta_i_df],axis=1)
sta_avg = sta_ind.apply(np.mean,axis=1) #average each row across columns
t_sta = np.arange(-40,5.1,0.1) #time from -50 to 10 ms inclusive by 0.1
t_df = pd.DataFrame(t_sta) #make a time data frame
channels[item][key]['STA'] = sta_avg
plt.plot(t_df,sta_avg,label='g'+item+'_bar*'+key) #easier to call as separate x,y
sta_i = None
sta_i_df = None
sta_ind = None
sta_avg = None
plt.legend()
#plt.show()
# plt.savefig(item+'_sta.png',dpi=600,format='png')
wns = item.iloc[:, i]
wns2 = AnalogSignal(signal=wns.values * pq.nA,
sampling_rate=10 * pq.kHz,
t_start=0 * pq.ms)
wns_list.append(wns2)
wns_list_list.append(wns_list)
#%%
#stavg = spike_triggered_average(wns_list[0],st_list[0],(-50*pq.ms,10.1*pq.ms))
#Set up a for loop to do an STA for all trials
sta_list = []
for k in range(16):
sta_ind = pd.DataFrame() #starting time column to build on
for i in range(50): #adding each columb of sta to df
sta_i = spike_triggered_average(wns_list_list[k][i],
spktrain_list[k][i],
(-50 * pq.ms, 10.1 * pq.ms))
sta_i_df = pd.DataFrame(sta_i.as_array().flatten())
sta_ind = pd.concat([sta_ind, sta_i_df], axis=1)
sta_avg = sta_ind.apply(np.mean, axis=1) #average each row across columns
t_sta = np.arange(-50, 10.1, 0.1) #time from -50 to 10 ms inclusive by 0.1
t_df = pd.DataFrame(t_sta) #make a time data frame
sta_list.append(sta_avg)
plt.figure()
plt.plot(t_df, sta_avg) #easier to call as separate x,y
#%%%
st_df_list = []
for item in binnedst_list:
st_df = pd.DataFrame(item.to_array()).T
st_df_list.append(st_df)
additional_inj0s = pd.DataFrame(
np.zeros(5000)) # making noise file match length of voltage file
additional_injtime = pd.DataFrame(np.arange(35000, 40000, 1))
inj_vmdf = pd.concat([inj_vmdf, additional_inj0s], axis=0)
inj_vmdf.index = np.arange(0, 40000)
os.chdir('../..')
wns = inj_vmdf.iloc[7000:32000, :]
wns2 = AnalogSignal(signal=wns.values * pq.nA,
sampling_rate=10 * pq.kHz,
t_start=700.0 * pq.ms)
sta_ind = pd.DataFrame() # starting time column to build on
for i in range(
len(del_half_neospiketrain_list)): # adding each columb of sta to df
sta_i = spike_triggered_average(wns2, del_half_neospiketrain_list[i],
(-50 * pq.ms, 10.1 * pq.ms))
sta_i_df = pd.DataFrame(sta_i.as_array().flatten())
sta_ind = pd.concat([sta_ind, sta_i_df], axis=1)
sta_avg = sta_ind.apply(np.mean, axis=1) # average each row across columns
t_sta = np.arange(-50, 10.1, 0.1) # time from -50 to 10 ms inclusive by 0.1
#sta_avg.index = t_sta
plt.figure()
plt.plot(t_sta, sta_avg) # easier to call as separate x,y
plt.xlabel('Time Before Spike (ms)')
plt.ylabel('Current (pA)')
plt.title('STA')
plt.show()
os.chdir('Analysis/STA')
plt.savefig('STA_' + file_name + '.png', bbox_inches='tight')
plt.close()
os.chdir('../..')
