def psd(datapdf):
d = data.read(result["path"])
nfft = 256 * 2
data2anz = result["attenddata"]
pow = zeros([(nfft / 2) + 1, size(data2anz, 1)])
comp = zeros([(nfft / 2) + 1, size(data2anz, 1)], complex_)
# fftreal = (zeros([(nfft/2)+1, size(attenddata,1)]))
for eachch in range(0, size(data2anz, 1)):
p, f, i = spectral.psd(
data2anz[:, eachch], NFFT=nfft, Fs=1 / d.hdr.header_data.sample_period
) # , noverlap=200)#, detrend=matplotlib.pylab.detrend_mean)
# p,f,i = spectral.psd(data[:,eachch], NFFT=nfft, Fs=1/ d.hdr.header_data.sample_period )
pow[:, eachch] = p
comp[:, eachch] = i
# fftreal[:,eachch] = r
logpow = 10 * log10(pow)
freq = f
def psd(datapdf):
d = data.read(datapdf)
nfft=256*2
ch = channel.index(datapdf, 'meg')
d.getdata(0, d.pnts_in_file, ch.channelindexhdr)
data2anz = d.data_block
#data2anz = result['attenddata']
pow = zeros([(nfft/2)+1, size(data2anz,1)])
comp = zeros([(nfft/2)+1, size(data2anz,1)], complex_)
#fftreal = (zeros([(nfft/2)+1, size(attenddata,1)]))
for eachch in range(0, size(data2anz,1)):
p,f,i = spectral.psd(data2anz[:,eachch], NFFT=nfft, Fs=1/ d.hdr.header_data.sample_period)#, noverlap=200)#, detrend=matplotlib.pylab.detrend_mean)
#p,f,i = spectral.psd(data[:,eachch], NFFT=nfft, Fs=1/ d.hdr.header_data.sample_period )
pow[:,eachch] = p
comp[:,eachch] = i
#fftreal[:,eachch] = r
logpow = 10*log10(pow)
freq=f
return logpow, freq