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chopin8.py
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#! /usr/bin/env python
import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import itemfreq
import numpy as np
import scipy.signal as sps
freq = (315.0 / 88.0) * 8.0
def doplot(B, A):
w, h = sps.freqz(B, A)
fig = plt.figure()
plt.title('Digital filter frequency response')
db = 20 * np.log10(abs(h))
ax1 = fig.add_subplot(111)
plt.plot(w * (freq/np.pi) / 2.0, 20 * np.log10(abs(h)), 'b')
plt.ylabel('Amplitude [dB]', color='b')
plt.xlabel('Frequency [rad/sample]')
ax2 = ax1.twinx()
angles = np.unwrap(np.angle(h))
plt.plot(w * (freq/np.pi) / 2.0, angles, 'g')
plt.ylabel('Angle (radians)', color='g')
plt.grid()
plt.axis('tight')
plt.show()
CD_BASE_FREQUENCY = 4321800.0 # Hz
SAMPLE_FREQUENCY = 28.636e6 # Hz
FREQ_MHZ = (315.0 / 88.0) * 8.0
NYQUIST_MHZ = FREQ_MHZ / 2
FREQ_HZ = FREQ_MHZ * 1000000.0
NYQUIST_HZ = FREQ_HZ / 2
data = np.fromfile("chopin8.cdraw", dtype = np.uint8)
# remove the first samples because they are strange (lower amplitude)
data = data[2650:len(data)-5000]
# This prints stuff out for LTspice
#for i in range(0, len(data)):
# print i / FREQ_HZ,",", (data[i] / 256.0) - .5
#
#exit()
# without filter: 299/964
# poles at 0 and 49700 hz, 3.202312738us, zero at 1.59mhz/0.100097448us
# this shhould be - but doesn't work worth a darn
deemp_pole = .100097448 * 1
deemp_zero = 3.202312738 * 1
# 21/1018
# NEW MEASURE: 850/1079
deemp_pole = .1100 * 1
deemp_zero = 3.100 * 1
lowpass_b, lowpass_a = sps.butter(2, 2.120/NYQUIST_MHZ)
# 999/1050
deemp_pole = .1450 * 1
deemp_zero = 3.100 * 1
lowpass_b, lowpass_a = sps.butter(2, 2.120/NYQUIST_MHZ)
# 1177/798
deemp_pole = .1450 * 1
deemp_zero = 3.100 * 1
lowpass_b, lowpass_a = sps.butter(4, 2.200/NYQUIST_MHZ)
# 1204/771
deemp_pole = .1450 * 1
deemp_zero = 3.202312738 * 1
lowpass_b, lowpass_a = sps.butter(4, 2.200/NYQUIST_MHZ)
# 1204/771
deemp_pole = .1450 * 1
deemp_zero = 3.202312738 * 1
lowpass_b, lowpass_a = sps.butter(4, 2.200/NYQUIST_MHZ)
# 1205/766
deemp_pole = .1450 * 1
deemp_zero = 3.30 * 1
lowpass_b, lowpass_a = sps.butter(4, 2.200/NYQUIST_MHZ)
# 1663/354
deemp_pole = .1450 * 1
deemp_zero = 3.20 * 1
lowpass_b = sps.firwin(43, [.400/NYQUIST_MHZ, 2.800/NYQUIST_MHZ], pass_zero=False)
lowpass_a = [1.0]
# 1664/351
deemp_pole = .1480 * 1
deemp_zero = 3.20 * 1
lowpass_b = sps.firwin(43, [.400/NYQUIST_MHZ, 2.800/NYQUIST_MHZ], pass_zero=False)
lowpass_a = [1.0]
# 1664/345
deemp_pole = .1480 * 1
deemp_zero = 3.20 * 1
lowpass_b = sps.firwin(45, [.390/NYQUIST_MHZ, 2.700/NYQUIST_MHZ], pass_zero=False)
lowpass_a = [1.0]
lowpass_b, lowpass_a = sps.butter(4, [0.39/NYQUIST_MHZ, 2.700/NYQUIST_MHZ], btype='bandpass')
# 1664/345
deemp_pole = .1480 * 1
deemp_zero = 3.20 * 1
lowpass_b = sps.firwin(45, [.390/NYQUIST_MHZ, 2.600/NYQUIST_MHZ], pass_zero=False)
lowpass_a = [1.0]
[tf_b, tf_a] = sps.zpk2tf([-deemp_pole*(10**-8)], [-deemp_zero*(10**-8)], deemp_pole / deemp_zero)
[f_emp_b, f_emp_a] = sps.bilinear(tf_b, tf_a, .5/FREQ_HZ)
# .295 leftover scale: 1053 frames found, 34731 good samps, 374 ERRORS
bandpass = sps.firwin(55, [.335/NYQUIST_MHZ, 1.870/NYQUIST_MHZ], pass_zero=False)
# .295 leftover scale: 1053 frames found, 34731 good samps, 374 ERRORS
# 1647/352
bandpass = sps.firwin(53, [.335/NYQUIST_MHZ, 1.870/NYQUIST_MHZ], pass_zero=False)
# 1651/348
bandpass = sps.firwin(53, [.355/NYQUIST_MHZ, 1.870/NYQUIST_MHZ], pass_zero=False)
# 1652/343
bandpass = sps.firwin(53, [.355/NYQUIST_MHZ, 2.800/NYQUIST_MHZ], pass_zero=False)
# 1660/341
bandpass = sps.firwin(39, [.600/NYQUIST_MHZ, 2.800/NYQUIST_MHZ], pass_zero=False)
# 1660/339
bandpass = sps.firwin(37, [.900/NYQUIST_MHZ, 2.800/NYQUIST_MHZ], pass_zero=False)
# 1663/348
bandpass = sps.firwin(43, [.880/NYQUIST_MHZ, 2.800/NYQUIST_MHZ], pass_zero=False)
#doplot(f_emp_b, f_emp_a)
#doplot(bandpass, [1.0])
#exit()
# convert to single-precision floats
#data = data.astype(np.float32)
# fewer errors if we filter as double precision
data = data.astype(np.float64)
# subtract DC component
dc = data.mean()
data -= dc
#plt.plot(data[5000:6000])
data = sps.lfilter(f_emp_b, f_emp_a, data)
data = sps.lfilter(lowpass_b, lowpass_a, data)
#data = sps.lfilter(bandpass, [1.0], data)
plt.plot(data[5000:6000])
plt.show()
#exit()
# filter to binary signal
data = (data > 0.0)
#for i in range(1, len(data) - 1):
# if data[i] == True and data[i - 1] == False and data[i + 1] == False:
# print "err ", i
# data[i] = False
# if data[i] == False and data[i - 1] == True and data[i + 1] == True:
# print "errp ", i
# data[i] = True
transition = (np.diff(data) != 0)
transition = np.insert(transition, 0, False) # The first sample is never a transition.
print "data", data.shape, data.dtype
print "transition", transition.shape, transition.dtype
runLengths = np.diff(np.where(transition)[0])
# fetch run signal values. The last transition
# isn't part of a well-defined run, so we don't need it.
runValues = data[transition].astype(np.int8)
runValues = runValues[:-1]
print "runLengths", runLengths.shape, runLengths.dtype
print "runValues", runValues.shape, runValues.dtype
totalRunlength0 = np.sum(runLengths[runValues == 0])
totalRunlength1 = np.sum(runLengths[runValues == 1])
bias = (totalRunlength0 - totalRunlength1) / (SAMPLE_FREQUENCY * len(runLengths))
print "bias: {} seconds".format(bias)
bias = 0
runDurations = runLengths / SAMPLE_FREQUENCY # to SECONDS
runDurations[runValues == 0] -= bias
runDurations[runValues == 1] += bias
runDurations = runDurations * CD_BASE_FREQUENCY # to CD BASE FREQUENCY TICKS
if False:
print "plotting ..."
freqAll = itemfreq(runDurations)
freq0 = itemfreq(runDurations[runValues == 0])
freq1 = itemfreq(runDurations[runValues == 1])
plt.subplot(411)
plt.title("All runs (bias corrected)")
plt.xlim(0, 13)
plt.plot(freqAll[:, 0], freqAll[:, 1], '.-')
plt.subplot(412)
plt.title("Bias-corrected zero runs ")
plt.xlim(0, 13)
plt.plot(freq0[:, 0], freq0[:, 1], '.-')
plt.subplot(413)
plt.title("Bias-corrected one runs")
plt.xlim(0, 13)
plt.plot(freq1[:, 0], freq1[:, 1], '.-')
plt.subplot(414)
plt.title("Bias-corrected zero/one runs, overlayed")
plt.xlim(0, 13)
plt.plot(freq0[:, 0], freq0[:, 1], '.-')
plt.plot(freq1[:, 0], freq1[:, 1], '.-')
plt.savefig("chopin8.pdf")
plt.close()
if True:
print "writing file ..."
with open("chopin8-bits.txt", "w") as f:
leftover = 0
for (value, duration) in zip(runValues, runDurations):
#durationr = int(round(duration + (leftover * .111))) # to integer
#durationr = int(round(duration + (leftover * 0.22))) # to integer
#durationr = int(round(duration + (leftover * 0.24))) # to integer
# durationr = int(round(duration + (leftover * 0.270))) # to integer
#durationr = int(round(duration + (leftover * 0.295))) # to integer
durationr = int(round(duration + (leftover * 0.299))) # to integer
# durationr = int(round(duration)) # to integer
leftover = duration - durationr
f.write(str(value) * durationr)