"""

Requires data stored in a time X channels X trials matrix.

Remove environmental noise with TSPCA (shifts=-50:50).

Remove sensor noise with SNS.

Remove non-repeatable components with DSS.

"""

# remove means

noisy_data = demean(data)[0]

noisy_ref = demean(ref)[0]

# apply TSPCA

shifts = arange(-50, 51)

print 'TSPCA ...'

data_tspca, idx = tsr(noisy_data, noisy_ref, shifts)[0:2]

data = data[idx, :, :]

data_mean = mean(data, 2)

data_tspca_mean = mean(data_tspca, 2)

# stats

#p1 = wpwr(data)[0]

#pp1 = wpwr(data_mean)[0]

#p2 = wpwr(data_tspca)[0]

#pp2 = wpwr(data_tspca_mean)[0]

#print "TSPCA done. ", 100*p2/p1, " of raw power remains"

#print "trial-averaged: ", 100*pp2/pp1, " of raw power remains"

# apply SNS

nneighbors = 10

print 'SNS ...'

data_tspca_sns = sns(data_tspca, nneighbors)

# apply DSS

print "DSS ..."

## Keep all PC components

data_tspca_sns = demean(data_tspca_sns)[0]

todss, fromdss, ratio, pwr = dss1(data_tspca_sns)

## c3 = DSS components

data_tspca_sns_dss = fold(dot(unfold(data_tspca_sns), todss), data_tspca_sns.shape[0]);