1 files changed, 93 insertions, 0 deletions
diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py
new file mode 100644
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+++ b/dpd/src/Dab_Util.py
@@ -0,0 +1,93 @@
+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)