def datatreatment(inputdata,inputtime):
import scipy.signal as signal
# baseline correction
inputdata = inputdata-np.mean(inputdata)
# tapering
window = signal.tukey(len(inputdata),alpha=0.15,sym=True)
inputdata = inputdata*window
# fft
dt = inputtime[1]-inputtime[0]
fftdata = np.fft.fft(inputdata)
# fftfreq = np.fft.fftfreq(len(fftdata),dt)
# cut the length
fftdata = fftdata[:int(len(fftdata)/2)]
df = 1./(inputtime[-1]-inputtime[0])
fftfreq = np.array([(i)*df for i in range(len(fftdata))])
fftamp = np.abs(fftdata)
#konno ohmachi smoothing
fftsmooth = konnoOhmachiSmoothing(fftamp,fftfreq)
return fftamp,fftfreq,fftsmooth
ds2.append(float(tmp[1]))
a1.plot(ts2,ds2-np.mean(ds2),'r',label='real surface recording',alpha=0.6)
a1.legend(loc='best')
fig1.tight_layout()
# fig1.savefig(dirprocess+os.sep+dirprocess+'_02.png')
a3.plot(ts2,ds2-np.mean(ds2),'r',label='real surface recording',alpha=0.6)
a3.legend(loc='best')
fig3.tight_layout()
# fig3.savefig(dirprocess+os.sep+dirprocess+'_01.png')
# calculation of observed transfer function
tf2 = np.abs(np.fft.fft(ds2-np.mean(ds2))/np.fft.fft(ds-np.mean(ds)))
tf2_freq = np.linspace(1./(ts2[-1]-ts2[0]),100.,len(ds2)/2)
tf2_smooth = konnoOhmachiSmoothing(tf2[:len(tf2_freq)],tf2_freq)
# testing to treat the data
def datatreatment(inputdata,inputtime):
import scipy.signal as signal
# baseline correction
inputdata = inputdata-np.mean(inputdata)
# tapering
window = signal.tukey(len(inputdata),alpha=0.15,sym=True)
inputdata = inputdata*window
# fft
dt = inputtime[1]-inputtime[0]
fftdata = np.fft.fft(inputdata)
# fftfreq = np.fft.fftfreq(len(fftdata),dt)
# cut the length
fftdata = fftdata[:int(len(fftdata)/2)]
