From 958c9920a5f05c97952f72c93b2e9dca231cfb8b Mon Sep 17 00:00:00 2001
From: Nao Pross <np@0hm.ch>
Date: Sat, 27 Nov 2021 19:57:03 +0100
Subject: Start access code cross correlation

---
 tests/correlator/acproc.py | 48 ++++++++++++++++++++++++++++++----------------
 1 file changed, 32 insertions(+), 16 deletions(-)

(limited to 'tests/correlator/acproc.py')

diff --git a/tests/correlator/acproc.py b/tests/correlator/acproc.py
index e119520..50c9a38 100755
--- a/tests/correlator/acproc.py
+++ b/tests/correlator/acproc.py
@@ -2,19 +2,21 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-import acgen
+from acgen import acgen
 
-# Parameters
-# samples per symbol
-sps = 4
-# number of initial bytes (to ignore)
-nzeros = 10
-# length of the access code in bytes
-aclen = 2
+# parameters
+access_code_bits = [ 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, ]
+access_code = list(np.packbits([0] * 3 + access_code_bits))
+padding_zeros = 10
 
 # Create samples
-print("Modulating symbols")
-acgen.main()
+print(f"Modulating symbols for access code {access_code_bits} = {access_code} with after {padding_zeros} empty bytes")
+gen = acgen()
+gen.set_access_code(access_code)
+gen.set_padding_zeros(padding_zeros)
+
+gen.start()
+gen.wait()
 
 # Extract one sequence
 print("Extracting symbol sequence")
@@ -27,17 +29,24 @@ plt.title("Raw Data (time domain)")
 plt.show()
 
 # take only symbols
-symbols = data[1::sps]
+symbols = data[1::gen.sps]
+
 plt.plot(symbols.real, symbols.imag)
 plt.title("Symbols only (constellation)")
 plt.show()
 
 # where ac symbols start, in symbols
-ac_start = nzeros * 8
-ac_end = ac_start + aclen * 8
+ac_start = (padding_zeros) * 8 + 3 # plus three because code is 13 bits
+ac_end = ac_start + int(np.ceil(len(access_code_bits) / 2.)) # divided by two because QPSK
 
 ac = symbols[ac_start:ac_end]
 
+print(f"Generated {len(ac)} (left padded) symbols from a {len(access_code_bits)} bit sequence")
+print(list(ac))
+
+print(f"Upsampled to {gen.sps} samples per symbos")
+print(sum([[i, i, i, i] for i in ac], []))
+
 fig, (ax1, ax2) = plt.subplots(2, 1)
 fig.tight_layout()
 
@@ -51,7 +60,14 @@ plt.show()
 
 fir = list(np.conj(ac[::-1]))
 
-# print the symbols
-print(f"Generated {len(ac)} symbols from a {aclen} byte sequence")
-print("Reversed symbols (for FIR filter):")
+# Print the symbols
+print("Reversed complex conjugate symbols (for FIR filter):")
 print(fir)
+
+# Compute cross correlation
+
+xc = np.convolve(fir, ac)
+
+plt.plot(np.abs(xc))
+plt.title("Cross correlation (FIR)")
+plt.show()
-- 
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