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-rwxr-xr-xtests/correlator/acproc.py73
1 files changed, 0 insertions, 73 deletions
diff --git a/tests/correlator/acproc.py b/tests/correlator/acproc.py
deleted file mode 100755
index 50c9a38..0000000
--- a/tests/correlator/acproc.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#!/usr/bin/env python3
-
-import numpy as np
-import matplotlib.pyplot as plt
-from acgen import acgen
-
-# 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(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")
-
-# raw data
-data = np.fromfile("acgen.dat", dtype=np.complex64)
-plt.plot(data.real)
-plt.plot(data.imag)
-plt.title("Raw Data (time domain)")
-plt.show()
-
-# take only symbols
-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 = (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()
-
-ax1.plot(ac.real, ac.imag)
-ax1.set_title("Symbols of Access Code (constellation)")
-
-ax2.plot(ac.real, ".-")
-ax2.plot(ac.imag, ".-")
-ax2.set_title("Symbols of Access Code (time)")
-plt.show()
-
-fir = list(np.conj(ac[::-1]))
-
-# 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()