def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
#TODO, must be updated so that samples betwen vector chunks are also interpolated
#Setup data to be resampled, if there is an old sample, add to beginning of vector
if self.lastIn is None:
interpSamples = signalIn.samples
else:
interpSamples = np.hstack(
(np.array([self.lastIn]), signalIn.samples))
timeAxis = np.arange(len(interpSamples)) / float(signalOut.rate)
self.lastIn = None #signalIn.samples[-1]
rsr = self.outputRate / float(signalIn.rate)
numOutSamp = np.floor(len(timeAxis) * rsr)
resampTime = np.arange(numOutSamp - rsr) / self.outputRate
interpolator = scipy.interpolate.interp1d(timeAxis,
interpSamples,
assume_sorted=False)
#print "ta", timeAxis
#print "sa", interpSamples
#print "rt", resampTime
#print "rsr", rsr
signalOut.samples = interpolator(resampTime)
signalOut.rate = self.outputRate
return signalOut
def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
phaseRate = 2.0 * np.pi * self.nco / signalIn.rate
phases = (self.ph + np.arange(len(signalIn.samples)) * phaseRate)
self.ph = phases[-1] + phaseRate
signalOut.shift += self.nco
signalOut.samples = signalIn.samples * np.exp(1j * phases)
return signalOut
def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
allsample = np.hstack((self.rest, signalIn.samples))
outSample = np.floor(len(allsample) / self.factor)
last = int(outSample * self.factor)
signalOut.samples = allsample[0:last:self.factor]
self.rest = allsample[last:]
signalOut.rate /= float(self.factor)
return signalOut
def process(self, signalIn):
samples = []
while samples == []:
samples = self.sndDev.getSamples(self.samples)
time.sleep(0.001)
#print samples
if len(samples.shape) > 1:
samples = samples[:,1]
return dspSignal.Signal(samples=samples, domain='time', fc=0, rate = 44000, shift = 0)
def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
#t = np.arange(len(signalIn.samples))/float(signalIn.rate)+1.0/signalIn.rate+self.tlast #time
rate = np.cumsum(np.real(signalIn.samples*self.s))/float(signalIn.rate)
cp = np.pi*2*rate+self.plast
#print cp
#signalOut.samples = signalIn.samples*np.exp(-1.0j*np.pi*2.0*t)
signalOut.samples = np.exp(1.0j*cp)
self.plast = cp[-1]%(2.0*np.pi)
return signalOut
def process(self, signalIn):
#time.sleep(0.001)
#tIdle = time.time()-self.tlast
#t0 = time.time()
samples = self.sdr.getSamples(self.samples)
if samples is None:
return None
return dspSignal.Signal(samples=samples,
domain='time',
fc=self.fc,
rate=self.rate,
shift=0)
def process(self, signalIn):
signalOut = signalIn
if not self.fig.isReady():
return signalOut
if self.nmax is None:
nsamp = len(signalIn.samples)
else:
nsamp = min(len(signalIn.samples), self.nmax)
signalOut = dspSignal.Signal(baseSig=signalIn)
ft = np.fft.fftshift(np.fft.fft(signalIn.samples[0:nsamp]))
db = np.clip(20 * np.log10(np.abs(ft)), -100, None)
fax = np.linspace(-signalIn.rate, signalIn.rate,
nsamp) - signalIn.shift
self.fig.plot(fax, db)
return signalOut
def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
ph = np.diff(np.unwrap(np.angle(signalIn.samples)))
signalOut.samples = ph
return signalOut
def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
signalOut.samples = signalIn.samples - np.mean(signalIn.samples)
return signalOut
def process(self, signalIn):
signalOut = dspSignal.Signal(baseSig=signalIn)
signalOut.samples = self.processSimple(signalIn.samples)
return signalOut
