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ADC Testing
Commit 17429fa0 authored by Federico Asara's avatar Federico Asara
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Removed useless matplotlib import. 

Signal class is as generic as possible.
SingleToneSignal reflects changes in Signal.
TwoTone signal computes IMD, but a mechanism to reduce incoherency is needed.
doubleSinefit4 fixed.
parent 4dd6d588
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Showing with 154 additions and 53 deletions
SignalProcessing/Signal.py
View file @ 17429fa0
......@@ -38,55 +38,18 @@ class Signal(object):
self.data = self.fulldata
self.nsamples = len(data)
if self.fullnsamples > 0:
# remove DC component
self.fulldata -= (max(self.fulldata) +min(self.fulldata))/2.
# calculate the |fft|
self.fulldft = abs(fft.fft(self.fulldata))
# useful names
N = len(data)
fdft = self.fulldft
# index of the biggest peak
first = 1. + argmax(fdft[1:N/2])
# index of the biggest peak nearest to `first`
# can only be first +-1.
second = first + (argmax(fdft[first-1:first+2:2])*2) -1
ratio = (fdft[second] / fdft[first])
# save first in self
self.first = first
# self.beta quantifies the sampling incoherency, defining the
# fraction of a period sampled in excess.
self.beta = N/pi * arctan(sin(pi/N)/(cos(pi/N)+1./ratio))
# the position the peak between first and second
self.w0index = first+ self.beta
# sampling frequency
freqSample = 2 * pi * self.rate
# initial frequency guess
w0 = freqSample * float(self.w0index)/self.nsamples
print "Frequency initial guess ->", w0
# fit the sine
self.w0, self.A, self.B, self.C = Sinefit.sinefit4(data, 1.0/rate, w0, 1e-7)
print "Frequency fit ->", self.w0
# limit data removing incoherency
self.w0index = self.w0 /freqSample * self.nsamples
self.limit = floor(0.5 + N*int(self.w0index)/self.w0index)
self.data = data[:self.limit]
self.nsamples = len(self.data)
print "limit is:", self.limit
self.data = self.fulldata
self.precalculateAll()
def report(self):
for i in self.items():
print "%s: %s" % (i[0], i[1] % i[2])
def items(self):
"""Create a list of tuples that describes the signal.
......@@ -101,9 +64,6 @@ class Signal(object):
output.append(('Number of bits', '%d b', self.nbits))
output.append(('Sampling rate', '%.2f Hz', self.rate))
output.append(('Number of samples', "%d samples", self.nsamples))
output.append(('Peak at', "%f", self.w0index))
output.append(('Input frequency', "%.6f Hz", self.w0/2/pi))
output.append(('Beta', "%f", self.beta))
return output
......
SignalProcessing/Sinefit.py
View file @ 17429fa0
......@@ -32,7 +32,7 @@ def doubleSinefit3(samples, sample_t, w0, w1):
x0 = (D0T * D0).I * D0T * matrix(samples).T
return array(x0).reshape((5,))
def doubleSinefit4matrix(samples, sample_t, w0, w1, tol=1e-7):
def doubleSinefit4matrix(samples, sample_t, w0, w1, tol=1e-9):
"""fit a sampled wave to a sine of unknown frequency
This routine implements the algorithm of IEEE 1241, sect. 4.1.4.3.,
......@@ -48,7 +48,7 @@ def doubleSinefit4matrix(samples, sample_t, w0, w1, tol=1e-7):
The return value is the quadruple (w0, a0, b0, c0)
"""
a0, b0, a1, b1, c0 = sinefit3(samples, sample_t, w0, w1)
a0, b0, a1, b1, c0 = doubleSinefit3(samples, sample_t, w0, w1)
deltaw0 = 0
deltaw1 = 0
......
SignalProcessing/SingleToneSignal.py
View file @ 17429fa0
......@@ -50,6 +50,9 @@ class SingleToneSignal(Signal):
def items(self):
output = super(SingleToneSignal, self).items()
output.append(('Peak at', "%f", self.w0index))
output.append(('Input frequency', "%.6f Hz", self.w0/2/pi))
output.append(('Beta', "%f", self.beta))
output.append(('Max DNL', "%f ", self.maxDNL))
output.append(('Max INL', "%f ", self.maxINL))
output.append(('Theoretical SNR', "%.2f dB", self.thSNR))
......@@ -61,7 +64,56 @@ class SingleToneSignal(Signal):
def precalculateAll(self):
"""Evaluates all the parameters of the signal, and also call the
precalculate method for each window function we know."""
if self.fullnsamples > 0:
# remove DC component
# self.fulldata -= (max(self.fulldata) +min(self.fulldata))/2.
# calculate the |fft|
self.fulldft = abs(fft.fft(self.fulldata))
# useful names
data = self.data
rate = self.rate
N = len(data)
fdft = self.fulldft
# index of the biggest peak
first = 1. + argmax(fdft[1:N/2])
# index of the biggest peak nearest to `first`
# can only be first +-1.
second = first + (argmax(fdft[first-1:first+2:2])*2) -1
ratio = (fdft[second] / fdft[first])
# save first in self
self.first = first
# self.beta quantifies the sampling incoherency, defining the
# fraction of a period sampled in excess.
self.beta = N/pi * arctan(sin(pi/N)/(cos(pi/N)+1./ratio))
# the position the peak between first and second
self.w0index = first+ self.beta
# sampling frequency
freqSample = 2 * pi * self.rate
# initial frequency guess
w0 = freqSample * float(self.w0index)/self.nsamples
print "Frequency initial guess ->", w0
# fit the sine
self.w0, self.A, self.B, self.C = Sinefit.sinefit4(data, 1.0/rate, w0, 1e-7)
print "Frequency fit ->", self.w0
# limit data removing incoherency
self.w0index = self.w0 /freqSample * self.nsamples
self.limit = floor(0.5 + N*int(self.w0index)/self.w0index)
self.data = data[:self.limit]
self.nsamples = len(self.data)
print "limit is:", self.limit
# First of all evaluate the histograms
skip = False
......
SignalProcessing/TwoToneSignal.py
View file @ 17429fa0
......@@ -4,6 +4,10 @@ class TwoToneSignal(Signal):
"""A class that represent a time-domain sampled signal with two sinusoids
with similiar frequenciess.
"""
m1 = 0
m2 = 0
tow1 = 0.
tow2 = 0.
def __init__(self, nbits = 0, rate = 1, data = []):
"""initialize a signal object
......@@ -19,11 +23,99 @@ class TwoToneSignal(Signal):
def items(self):
output = super(TwoToneSignal, self).items()
output.append(('The cake is a lie', "%s ", True))
output.append(('Peak 1 at', "%d bin", self.m1))
output.append(('Peak 2 at', "%d bin", self.m2))
output.append(('Guess for wave 1', "%f Hz", self.tow1/2./pi))
output.append(('Guess for wave 2', "%f Hz", self.tow2/2./pi))
output.append(('Sine 1 frequency', "%f Hz", self.w1/2./pi))
output.append(('Sine 1 A', "%f", self.a1))
output.append(('Sine 1 B', "%f", self.b1))
output.append(('Sine 2 frequency', "%f Hz", self.w2/2./pi))
output.append(('Sine 2 A', "%f", self.a2))
output.append(('Sine 2 B', "%f", self.b2))
output.append(('DC component', "%f", self.c0))
output.append(('IMD', "%f dBc", self.imd))
return output
def toi(self, x):
return self.nsamples*x/2/pi/self.rate
def precalculateAll(self):
"""Evaluates all the parameters of the signal, and also call the
precalculate method for each window function we know."""
# ok, compute the FFT
ff = abs(fft.fft(self.fulldata))
lbnd = max(ff) * 10e-12
self.fft = ff = where(ff < lbnd, 10e-12, ff)
hff = ff[:len(ff)/2]
self.lfft = lff = 10*log10(ff)
self.m1 = m1 = argmax(hff);
self.m2 = m2 = argmax(hstack([hff[:m1-1], array([0, 0, 0]), hff[m1+2:]]))
self.tow1 = tow1 = 2*pi*self.rate*float(m1)/self.nsamples
self.tow2 = tow2 = 2*pi*self.rate*float(m2)/self.nsamples
(self.w1, self.a1, self.b1), (self.w2, self.a2, self.b2), self.c0 = \
Sinefit.doubleSinefit4matrix(self.fulldata, self.rate**-1, tow1, tow2)
delta = abs(self.w1 - self.w2)
i1 = min([self.w1, self.w2]) - delta
i2 = max([self.w1, self.w2]) + delta
fw1, fw2 = self.fft[self.toi(self.w1)], self.fft[self.toi(self.w2)]
fi1, fi2 = max(self.fft[self.toi(i1)-1:self.toi(i1)+2]), max(self.fft[self.toi(i2)-1:self.toi(i2)+2])
print fw1, fw2
print fi1, fi2
meaningful = hypot(fw1, fw2)
interferences = hypot(fi1, fi2)
self.imd = 10*log10(meaningful/interferences)
# fix incoherency
self.report()
fd = abs(self.w1 + self.w2)/4./pi
print "fd is", fd
wModIndex = self.nsamples * fd / self.rate
print "wModIndex is", wModIndex
factor = (wModIndex -(wModIndex % 2))/wModIndex
limit = floor(self.nsamples*factor)
print "limit is", limit
return
self.data = self.fulldata[:limit]
self.nsamples = limit
ff = abs(fft.fft(self.data))
lbnd = max(ff) * 10e-12
self.fft = ff = where(ff < lbnd, 10e-12, ff)
hff = ff[:len(ff)/2]
self.lfft = lff = 10*log10(ff)
self.m1 = m1 = argmax(hff);
self.m2 = m2 = argmax(hstack([hff[:m1-1], array([0, 0, 0]), hff[m1+2:]]))
self.tow1 = tow1 = 2*pi*self.rate*float(m1)/self.nsamples
self.tow2 = tow2 = 2*pi*self.rate*float(m2)/self.nsamples
(self.w1, self.a1, self.b1), (self.w2, self.a2, self.b2), self.c0 = \
Sinefit.doubleSinefit4matrix(self.data, self.rate**-1, tow1, tow2)
fd = abs(self.w1 - self.w2)/2./pi
print "fd is", fd
return
SignalProcessing/__init__.py
View file @ 17429fa0
......@@ -2,6 +2,3 @@ __author__="federico"
__date__ ="$Jul 11, 2011 2:39:08 PM$"
import matplotlib
matplotlib.use('Qt4Agg')
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