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import numpy as np
import scipy
import matplotlib.pyplot as plt
import src.subsample_align as sa
from scipy import signal
import logging
class Dab_Util:
"""Collection of methods that can be applied to an array
complex IQ samples of a DAB signal
"""
def __init__(self, sample_rate):
"""
:param sample_rate: sample rate [sample/sec] to use for calculations
"""
self.sample_rate = sample_rate
self.dab_bandwidth = 1536000 #Bandwidth of a dab signal
self.frame_ms = 96 #Duration of a Dab frame
def lag(self, sig_orig, sig_rec):
"""
Find lag between two signals
Args:
sig_orig: The signal that has been sent
sig_rec: The signal that has been recored
"""
off = sig_rec.shape[0]
c = signal.correlate(sig_orig, sig_rec)
return np.argmax(c) - off + 1
def lag_upsampling(self, sig_orig, sig_rec, n_up):
sig_orig_up = signal.resample(sig_orig, sig_orig.shape[0] * n_up)
sig_rec_up = signal.resample(sig_rec, sig_rec.shape[0] * n_up)
l = self.lag(sig_orig_up, sig_rec_up)
l_orig = float(l) / n_up
return l_orig
def subsample_align_upsampling(self, sig1, sig2, n_up=32):
"""
Returns an aligned version of sig1 and sig2 by cropping and subsample alignment
Using upsampling
"""
assert(sig1.shape[0] == sig2.shape[0])
if sig1.shape[0] % 2 == 1:
sig1 = sig1[:-1]
sig2 = sig2[:-1]
sig1_up = signal.resample(sig1, sig1.shape[0] * n_up)
sig2_up = signal.resample(sig2, sig2.shape[0] * n_up)
off_meas = self.lag_upsampling(sig2_up, sig1_up, n_up=1)
off = int(abs(off_meas))
if off_meas > 0:
sig1_up = sig1_up[:-off]
sig2_up = sig2_up[off:]
elif off_meas < 0:
sig1_up = sig1_up[off:]
sig2_up = sig2_up[:-off]
sig1 = signal.resample(sig1_up, sig1_up.shape[0] / n_up).astype(np.complex64)
sig2 = signal.resample(sig2_up, sig2_up.shape[0] / n_up).astype(np.complex64)
return sig1, sig2
def subsample_align(self, sig1, sig2):
"""
Returns an aligned version of sig1 and sig2 by cropping and subsample alignment
"""
logging.debug("Sig1_orig: %d %s, Sig2_orig: %d %s" % (len(sig1), sig1.dtype, len(sig2), sig2.dtype))
off_meas = self.lag_upsampling(sig2, sig1, n_up=1)
off = int(abs(off_meas))
if off_meas > 0:
sig1 = sig1[:-off]
sig2 = sig2[off:]
elif off_meas < 0:
sig1 = sig1[off:]
sig2 = sig2[:-off]
if off % 2 == 1:
sig1 = sig1[:-1]
sig2 = sig2[:-1]
sig2 = sa.subsample_align(sig2, sig1)
logging.debug("Sig1_cut: %d %s, Sig2_cut: %d %s, off: %d" % (len(sig1), sig1.dtype, len(sig2), sig2.dtype, off))
return sig1, sig2
def fromfile(self, filename, offset=0, length=None):
if length is None:
return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset)
else:
return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset, shape=length)
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