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ADC Testing
Commit 5f4c89b1 authored by Juan David González Cobas's avatar Juan David González Cobas
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Temporary commit of changes made to the DNL/INL computations

parent 7049c394
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Showing with 77 additions and 8 deletions
Signal.py
View file @ 5f4c89b1
......@@ -35,6 +35,7 @@ class Signal(object):
self.nbits = nbits
self.rate = rate
self.data = data
self.nsamples = len(data)
def histogram_resolution(self):
bins = 2**self.nbits
......@@ -48,9 +49,28 @@ class Signal(object):
returns: an array of 2**signal.nbits numbers (frequencies)
"""
bins = self.histogram_resolution()
hist, bins = histogram(array(self.data), bins)
return hist
# bins = self.histogram_resolution()
# hist, bins = histogram(array(self.data), bins)
bins = 2**self.nbits
hist, discard = histogram(array(self.data), bins)
return hist[1:-1]
def _ideal_histogram(self):
"""Produce an ideal vector of frequencies (histogram) for the
nsamples samples of a perfect nbits ADC. Mostly for auxiliary and
display purposes
returns: an array of 2**signal.nbits numbers (frequencies)
"""
Mt = len(self.data)
A = sin(pi/2 * Mt / (Mt + self.data[0] + self.data[-1]))
range = 2**self.nbits
midrange = range/2
n = arange(1, range-1)
p = arcsin(A/midrange * (n - midrange))
q = arcsin(A/midrange * (n - 1 - midrange))
p = (p - q) / pi
return Mt * p
def ideal_histogram(self):
"""Produce an ideal vector of frequencies (histogram) for the
......@@ -59,9 +79,18 @@ class Signal(object):
returns: an array of 2**signal.nbits numbers (frequencies)
"""
bins = self.histogram_resolution()
x = linspace(-1.0, 1.0, num=bins)
return (1/pi) / sqrt(1-x**2)
Mt = len(self.data)
A = sin(pi/2 * Mt / (Mt + self.data[0] + self.data[-1]))
range = 2**self.nbits
midrange = range/2
n = arange(1, range-1)
p = arcsin(A/midrange * (n - midrange))
q = arcsin(A/midrange * (n - 1 - midrange))
p = (p - q) / pi
# t = linspace(-1, 1, 2**self.nbits)[1:-1]
# print sum(1/pi * 1/sqrt(1-t**2) / 2**self.nbits * Mt)
# return 1/pi * 1/sqrt(1-t**2) / 2**self.nbits * Mt
return Mt * p
def DNL(self):
"""Compute differential non-linearity vector for a given time-domain
......@@ -71,7 +100,11 @@ class Signal(object):
- dnl is an array of 2**signal.nbits real values and
- total is a real value (computed from dnl)
"""
return [3] * (2 * self.nbits), 0.3
ideal = self.ideal_histogram()
real = self.histogram()
print size(ideal), size(real)
dnl = real/ideal - 1
return dnl, max(abs(dnl))
def INL(self):
......@@ -81,7 +114,9 @@ class Signal(object):
- inl is an array of 2**signal.nbits real values and
- total is a real (computed from inl)
"""
return [4] * (2 * self.nbits), 0.4
dnl, discard = self.DNL()
inl = cumsum(dnl)
return inl, max(abs(inl))
def FFT(self, navg, window):
"""Compute the amplitudes (in dB) of the FFT of signal, averaging navg
......@@ -93,3 +128,37 @@ class Signal(object):
returns: an FFTSignal object
"""
return FFTSignal(self.data, 1, 1)
def makesine(samples, periods, bits, amplitude=1, noise=0):
t = arange(samples)/float(samples)
sine = amplitude * sin(2*pi*periods*t)
sine += noise * ((t % 0.02) / 0.02 - 0.01)
sine = (sine * 2**bits + 0.5).astype(int)
place(sine, sine >= 2**bits, 2**bits)
place(sine, sine <= -2**bits, -2**bits)
out = file('data', 'w')
for datum in sine:
out.write(str(datum) + '\n')
out.close()
return sine
if __name__ == '__main__':
from matplotlib import pyplot
bits = 12
makesine(20000, 20, bits, 1.1)
f = [ int(sample) for sample in file('data')]
s = Signal(nbits = bits, rate = 123, data = f)
ideal = s.ideal_histogram()
real = s.histogram()
# pyplot.plot(ideal)
# pyplot.plot(real)
dnl = real/ideal-1
pyplot.plot(dnl)
pyplot.plot(cumsum(dnl))
# pyplot.plot(f)
pyplot.show()
print dnl[0:5], dnl[-5:]
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