def synchrony_stats(V, dt, maxlags=3000):
"""
Synchrony measures
Parameters
----------
V: numneuron x time
maxlags: maximal lag for autocorrelation, default = 3000 ms
Returns
-------
chi: synchrony measure
autocorr: autocorrelation of population avg \bar{V}(t)
"""
Vpop = np.mean( V, axis=0 ) # pop avg
sigmaV = np.mean( np.square(Vpop) ) - np.square( np.mean(Vpop) )
sigmaVi = np.mean( np.square(V), axis=1 ) - np.square( np.mean(V, axis=1) )
sigmaViMean = np.mean( sigmaVi )
chisq = sigmaV / sigmaViMean
chi = np.sqrt( chisq )
autocorr = acorr( Vpop - np.mean(Vpop), onesided=True, scale='coeff' )
return chi, autocorr
def synchrony_stats(V, dt, maxlags=3000):
"""
Synchrony measures
Parameters
----------
V: numneuron x time
maxlags: maximal lag for autocorrelation, default = 3000 ms
Returns
-------
chi: synchrony measure
autocorr: autocorrelation of population avg \bar{V}(t)
"""
Vpop = np.mean(V, axis=0) # pop avg
sigmaV = np.mean(np.square(Vpop)) - np.square(np.mean(Vpop))
sigmaVi = np.mean(np.square(V), axis=1) - np.square(np.mean(V, axis=1))
sigmaViMean = np.mean(sigmaVi)
chisq = sigmaV / sigmaViMean
chi = np.sqrt(chisq)
autocorr = acorr(Vpop - np.mean(Vpop), onesided=True, scale='coeff')
return chi, autocorr
def synchrony_stats(data, dt, maxlags=3000):
'''
Synchrony measures
Parameters
==========
data: numneuron x time
maxlags: maximal lag for autocorrelation, default = 3000 ms
Returns
=======
chi: synchrony measure
autocorr: autocorrelation of population avg \bar{data}(t)
'''
data_pop = np.mean(data, axis=0) # pop avg
sigma_pop = np.mean(np.square(data_pop)) - np.square(np.mean(data_pop))
sigma = np.mean(np.square(data), axis=1) - np.square(np.mean(data, axis=1))
sigma_mean = np.mean(sigma)
chisq = sigma_pop / sigma_mean
chi = np.sqrt(chisq)
autocorr = acorr(data_pop - np.mean(data_pop), onesided=True, scale='coeff')
return chi, autocorr
def synchrony_stats(data, dt, maxlags=3000):
'''
Synchrony measures
Parameters
==========
data: numneuron x time
maxlags: maximal lag for autocorrelation, default = 3000 ms
Returns
=======
chi: synchrony measure
autocorr: autocorrelation of population avg \bar{data}(t)
'''
data_pop = np.mean(data, axis=0) # pop avg
sigma_pop = np.mean(np.square(data_pop)) - np.square(np.mean(data_pop))
sigma = np.mean(np.square(data), axis=1) - np.square(np.mean(data, axis=1))
sigma_mean = np.mean(sigma)
chisq = sigma_pop / sigma_mean
chi = np.sqrt(chisq)
autocorr = acorr(data_pop - np.mean(data_pop),
onesided=True,
scale='coeff')
return chi, autocorr
# y[i] = lfilter(b[i], a[i], x[i]) # return y # # def f2(b, a, x): # return lfilter2(b, a, x) # # nframes = 3600 # a = np.random.randn(nframes, 1) # #a = np.array([[1], [-1]]) # b = np.random.randn(nframes, 24) # #b = np.array([[1, 1], [1, 1]]) # x = np.random.randn(nframes, 256) # #x = np.linspace(0, 19, 20).reshape((2, 10)) # # y = f1(b, a, x) # yr = f2(b, a, x) # np.testing.assert_array_almost_equal(y, yr) x = np.random.randn(1, 5) maxlag = 1 onesided = False axis = 1 nx = x.shape[1] y = cacorr(x, maxlag, onesided, axis) r_y = acorr(x, axis, onesided) #print y #print r_y np.testing.assert_array_almost_equal(y, r_y[:,nx-maxlag-1:nx+maxlag])
