def show_xcorr(cells):
n = len(cells)
maxlag = 3
binsz = 0.1
bins = np.arange(-maxlag, maxlag, 0.01)
ax=None
for i in range(n):
for j in range(i,n):
if i<>j:
ax = plt.subplot(n,n,i+j*n+1, sharey=ax)
crosscorrelate(cells[i]['spt'], cells[j]['spt'], maxlag,
display=ax,
kwargs={"bins":bins})
plt.axvline(0, color='k')
if ax:
ax.set_ylabel("")
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlabel("")
else:
ax_label = plt.subplot(n,n,i+j*n+1, frameon=False)
ax_label.set_xticks([])
ax_label.set_yticks([])
ax_label.text(0,0, cells[j]['dataset'],
transform=ax_label.transAxes,
rotation=45, ha='left',
va='bottom')
ax.set_xticks((-maxlag, maxlag))
ymin, ymax = ax.get_ylim()
ax.set_yticks((ymin, ymax))
def show_xcorr(cells):
n = len(cells)
maxlag = 3
binsz = 0.1
bins = np.arange(-maxlag, maxlag, 0.01)
ax=None
for i in range(n):
for j in range(i,n):
if i<>j:
ax = plt.subplot(n,n,i+j*n+1, sharey=ax)
crosscorrelate(cells[i]['spt'], cells[j]['spt'], maxlag,
display=ax,
kwargs={"bins":bins})
plt.axvline(0, color='k')
if ax:
ax.set_ylabel("")
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlabel("")
else:
ax_label = plt.subplot(n,n,i+j*n+1, frameon=False)
ax_label.set_xticks([])
ax_label.set_yticks([])
ax_label.text(0,0, cells[j]['dataset'],
transform=ax_label.transAxes,
rotation=45, ha='left',
va='bottom')
ax.set_xticks((-maxlag, maxlag))
ymin, ymax = ax.get_ylim()
ax.set_yticks((ymin, ymax))
def testCrosscorrelateLag500(self):
"""Test case with lag is higher than the trial length
"""
int, int_, norm = analysis.crosscorrelate(self.spk0, self.spk1,
lag=500.0)
matlab_int = numpy.loadtxt(self.p + '/analysis/crosscorrelate/out_matlab_int_lag_500')
numpy.testing.assert_array_almost_equal(int, matlab_int, decimal = 3)
def testCrosscorrelateLag100(self):
"""Test case with lag within the length of the input array
"""
int, int_, norm, = analysis.crosscorrelate(self.spk0, self.spk1,
lag=100.0)
matlab_int = numpy.loadtxt(self.p + '/analysis/crosscorrelate/out_matlab_int_lag_100')
numpy.testing.assert_array_almost_equal(int, matlab_int, decimal = 3)
def testCrosscorrelateLag500(self):
"""Test case with lag is higher than the trial length
"""
int, int_, norm = analysis.crosscorrelate(self.spk0,
self.spk1,
lag=500.0)
matlab_int = numpy.loadtxt(
self.p + '/analysis/crosscorrelate/out_matlab_int_lag_500')
numpy.testing.assert_array_almost_equal(int, matlab_int, decimal=3)
def testCrosscorrelateLag100(self):
"""Test case with lag within the length of the input array
"""
int, int_, norm, = analysis.crosscorrelate(self.spk0,
self.spk1,
lag=100.0)
matlab_int = numpy.loadtxt(
self.p + '/analysis/crosscorrelate/out_matlab_int_lag_100')
numpy.testing.assert_array_almost_equal(int, matlab_int, decimal=3)
def testCrosscorrelateNoLag(self):
int, int_, norm = analysis.crosscorrelate(self.spk0, self.spk1)
#The following are output was generated with the FIND MATLAB toolbox
matlab_int = numpy.loadtxt(self.p + '/analysis/crosscorrelate/out_matlab_int')
numpy.testing.assert_array_almost_equal(int, matlab_int,
decimal=3)
locs = (numpy.where(spiketrain[:,0] == cell+1))[0].tolist() #where returns tuple
indiv_trains.append(spiketrain[locs].tolist())
print indiv_trains
print "crosscorrelation"
#Pairwise kappa
ccmat = numpy.zeros((cells,cells))
for cell1 in range(cells):
for cell2 in range(cells):
#print numpy.array(indiv_trains[cell1])
st1 = signals.SpikeTrain(numpy.array(indiv_trains[cell1]).flatten())
st2 = signals.SpikeTrain(numpy.array(indiv_trains[cell2]).flatten())
#print st1, st2
#analysis.crosscorrelate(st1,st2,display=True,lag=1,kwargs={'bins':1000}) #plot requires display=True
#pylab.show()
out=analysis.crosscorrelate(st1,st2,lag=delta_t/2.,display=False) #return requires display=False
#print out[0]
ccmat[cell1,cell2] = (out[0].shape)[0]
#Check that it performed correctly
cell1=9
cell2=9
print len(indiv_trains[1]), len(indiv_trains[cell2]), ccmat[cell1,cell2], cell1, cell2
#Get kappa_ii
kappa_ii = ccmat.diagonal()
print kappa_ii
kappamat = numpy.zeros((cells,cells))
for cell1 in range(cells):
for cell2 in range(cells):
def testCrosscorrelateNoLag(self):
int, int_, norm = analysis.crosscorrelate(self.spk0, self.spk1)
#The following are output was generated with the FIND MATLAB toolbox
matlab_int = numpy.loadtxt(self.p +
'/analysis/crosscorrelate/out_matlab_int')
numpy.testing.assert_array_almost_equal(int, matlab_int, decimal=3)
