def getderivedlabels(datafile):
ch = channel.index(datafile, 'derived')
chdict = {}
for i in ch.chnumber:
chdict[ch.cfgchname[ch.cfgchannumber == i][0]] = i
return ch, chdict
def __init__(self, datapdf, chlabel):
ch=channel.index(datapdf, 'meg')
chind=ch.channelindexhdr[ch.channelsortedlabels == chlabel]
self.d=data.read(datapdf)
self.d.getdata(0, self.d.pnts_in_file, chind)
self.d.data_block=offset.correct(self.d.data_block)
print self.d.data_block.shape
self.p=pdf.read(datapdf)
self.ext = 'pymtimef'
def coilpower(self, event): # wxGlade: MyFrame.<event_handler>
print "Event handler `coilpower' "
try:
n = nearest(self.freq, 165)
except AttributeError:
print 'no power... running for you'
self.pow,self.freq = functions.psd(self.datapdf)
n = nearest(self.freq, 165)
self.ch = channel.index(self.datapdf, 'meg')
megcontour.display(self.pow[int(n[0]),:], self.ch.chanlocs)
def badchannels(self, event): # wxGlade: MyFrame.<event_handler>
print "Event handler `badchannels' "
try:
bad = badchannels.calc(datapdf, self.pow, ch,thresh=5, chfreq='yes', freqarray=self.freq, maxhz=200,powernotch='yes', minhz=4)
except NameError:
print 'no power... running for you'
self.pow,self.freq = functions.psd(self.datapdf)
self.ch = channel.index(self.datapdf, 'meg')
bad = badchannels.calc(self.datapdf, self.pow, self.ch, thresh=5, chfreq='yes', freqarray=self.freq, maxhz=200,powernotch='yes', minhz=4)
figure(1,figsize=(5,10))
subplot(2,1,1);plot(bad[3]);title('channel power ratio to family');
subplot(2,1,2);megcontour.display(bad[3], self.ch.chanlocs)
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
def getdata(datapath, chtype):
ch = channel.index(datapath, chtype)
d = data.read(datapath)
labels = ch.chlabel[ch.channelsind]
d.getdata(0, d.pnts_in_file, ch.channelsind)
return d, labels