def load_input_data(tf):
MAT = loadmat('/home/phil/matlab.mat')
noise = MAT['noise'].T
signal = MAT['signal'].T
nc = noise.shape[1]
ce = TimeSeriesCovE.white_noise_init(tf, nc, std=.98)
temps_ml = MAT['T']
temps = sp.empty((temps_ml.shape[0], temps_ml.shape[1] / nc, nc))
for i in xrange(temps_ml.shape[0]):
temps[i] = mcvec_from_conc(temps_ml[i], nc=nc)
return signal, noise, ce, temps
def get_input_data(tf):
noise = loadmat('/home/phil/matlab.mat')['noise'].T
nc = noise.shape[1]
spike_proto_sc = sp.cos(sp.linspace(-sp.pi, 3 * sp.pi, tf))
spike_proto_sc *= sp.hanning(tf)
scale = sp.linspace(0, 2, tf)
cvals = [(5., .5), (4., 9.), (3., 3.), (7., 2.5)]
xi1 = sp.vstack([spike_proto_sc * cvals[i][0] * scale
for i in xrange(nc)]).T
xi2 = sp.vstack([spike_proto_sc * cvals[i][1] * scale[::-1]
for i in xrange(nc)]).T
temps = sp.asarray([xi1, xi2])
ce = TimeSeriesCovE.white_noise_init(tf, nc, std=.98)
signal = sp.zeros_like(noise)
NPOS = 4
LEN = len(noise)
POS = [(int(i * LEN / (NPOS + 1)), 100) for i in xrange(1, NPOS + 1)]
POS.append((100, 2))
POS.append((150, 2))
print POS
for pos, tau in POS:
signal[pos:pos + tf] += temps[0]
signal[pos + tau:pos + tau + tf] += temps[1]
return signal, noise, ce, temps
