def fhn_timeseries(simfile):
# load simfile as numpy matrix
# extract first column of simout as time vector
# read u_i time series from simout
simout = sb.load_matrix(simfile)
# extract time vector and dt
tvec = simout[:, 0]
dt = tvec[1] - tvec[0]
T = int(math.ceil((tvec[-1]) / dt * params.dt))
# extract u-columns
u_indices = np.arange(1, simout.shape[1], 1)
timeseries = simout[:, u_indices]
print "extracted u-timeseries: shape =", timeseries.shape, ", dt = ", dt
#np.savetxt('u_timeseries_python.dat',timeseries,fmt='%.6f',delimiter='\t')
return timeseries, T
def fhn_timeseries(simfile):
# load simfile as numpy matrix
# extract first column of simout as time vector
# read u_i time series from simout
simout = sb.load_matrix(simfile)
# extract time vector and dt
tvec = simout[:,0]
dt = tvec[1] - tvec[0]
T = int(math.ceil( (tvec[-1]) / dt * params.dt ))
# extract u-columns
u_indices = np.arange(1, simout.shape[1] ,1)
timeseries = simout[:, u_indices]
print "extracted u-timeseries: shape =", timeseries.shape, ", dt = ", dt
#np.savetxt('u_timeseries_python.dat',timeseries,fmt='%.6f',delimiter='\t')
return timeseries, T
Y_fft = np.fft.fft(Y , m_pow) /float(m) Y_fft = 2*abs(Y_fft[0:m_pow /2 +1]) # frequency domain [Hz] freq = float(f_s)/2 * np.linspace(0,1, m_pow/2 + 1); return Y_fft, freq # user defined input name if __name__ == '__main__': try: input_name = sys.argv[1] except: sys.exit(1) data_matrix = sb.load_matrix(input_name) out_basename = sb.get_dat_basename(input_name) corr_matrix = correl_matrix(data_matrix , out_basename) ## if data is already a correlation matrix : #corr_matrix = data_matrix image = plot_corr_diag(corr_matrix, out_basename) # real node index : add 1! [i, j, k , l ] = node_index(corr_matrix) # BOLD activity of the nodes correlating the best pl.figure(2) plot_bold_signal(data_matrix, i,j) # BOLD activity of the nodes correlating the worst pl.figure(3) plot_bold_signal(data_matrix, k,l)
