def findFringes(filename):
"""
Find fringes in the scanning file 'filename'. Plots the results and returns
the value of the offset.
"""
f = pyfits.open(filename)
fringes = (f['IMAGING_DATA_FSUA'].data['DATA1']-f['IMAGING_DATA_FSUA'].data['DATA3'])[:,0]
snr = f['IMAGING_DATA_FSUA'].data['PDSNR']
opd = np.interp(f['IMAGING_DATA_FSUA'].data['TIME'],
f['OPDC'].data['TIME'], f['OPDC'].data['RTOFFSET'])
s = opd.argsort()
fringes = fringes[s]
snr = snr[s]
opd = opd[s]
plt.figure(0)
plt.clf()
ax=plt.subplot(211)
plt.plot(opd, fringes, label='A-C')
plt.legend()
plt.subplot(212, sharex=ax)
rms = slidop.slidingStd(opd,fringes, 10e-6)
rms/= rms.mean()
plt.plot(opd, rms, 'r', label='A-C RMS')
plt.plot(opd, snr, 'g', label='PDSNR')
plt.legend()
f.close()
return opd[rms.argmax()]
def ftkPerfo(filename, minState=5):
"""
estimate OPDC performance as OPD residuals
"""
f = pyfits.open(filename)
x = f['OPDC'].data.field('TIME')*1e-6
y = f['OPDC'].data.field('UWPHASE')
s = f['OPDC'].data.field('STATE')
f.close()
x = x[np.where(s>=minState)]
y = y[np.where(s>=minState)]
y = y[np.where(1-np.isnan(x))]
x = x[np.where(1-np.isnan(x))]
x = x[np.where(1-np.isnan(y))]
y = y[np.where(1-np.isnan(y))]
x = x[::3]
y = y[::3]
plt.figure(0)
plt.clf()
#plt.plot(x,y,'+', label='raw')
all_dx = np.logspace(-1,0.5,10)
jitt = []
for dx in all_dx:
try:
yp = slidop.slidingStd(1.0*x,1.0*y,dx)*2.2/(2*np.pi)
except:
print np.diff(x)
y_,yp,x_ = slidop.sliding_avg_rms(y*2.2/(2*np.pi),x,dx)
print yp
yp = np.interp(x,x_,yp)
jitt.append(yp.mean())
plt.plot(all_dx,jitt, 'ok-', label='residual jitter')
plt.ylabel('opd residual RMS (um)')
plt.xlabel('time window')
plt.xscale('log')
plt.vlines(21*0.040, plt.ylim()[0], plt.ylim()[1], label='MIDI frame')
plt.legend()
#plt.yscale('log')
return
