Fix correlation against phase ref

Also remove the old autocorrelation scripts, not pursuing this approach

1 files changed, 0 insertions, 77 deletions

diff --git a/autocorrelate_window.py b/autocorrelate_window.py
deleted file mode 100755
index cf69a77..0000000
--- a/autocorrelate_window.py
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/usr/bin/env python
-#
-# Do a set of autocorrelations over the test.16.14.25.iq file
-#
-# Licence: see LICENCE file
-#
-# hackrf_transfer example:
-# hackrf_transfer -r hackrf_dab.iq -f 211648000 -s 8 -n 768000
-
-import numpy as np
-import matplotlib.pyplot as pp
-import sys
-
-if len(sys.argv) != 3:
-    print("Usage")
-    print(" script [f64|u8] <filename>")
-    print(" fc64: file is 32-bit float I + 32-bit float Q")
-    print(" u8:   file is 8-bit unsigned I + 8-bit unsigned Q")
-    sys.exit(1)
-
-file_in = sys.argv[2]
-
-if sys.argv[1] == "u8":
-    channel_out1 = np.fromfile(file_in, np.uint8)
-    print("Convert u8 IQ to fc64 IQ")
-    channel_out2 = channel_out1.reshape(2, len(channel_out1)/2)
-    channel_out3 = channel_out2[0,...] + 1j * channel_out2[1,...]
-    channel_out = channel_out3.astype(np.complex64) / 256.0 - (0.5+0.5j)
-elif sys.argv[1] == "fc64":
-    channel_out = np.fromfile(file_in, np.complex64)
-
-channel_out = channel_out[:channel_out.size / 4]
-
-print("Autocorrelating")
-
-correlationlength = 500
-
-def autocorrelate(x, length):
-    return np.array([1] + [np.abs(np.corrcoef(x[:-i], x[i:])[0,1]) for i in range(1, length)])
-
-
-reshape_width = correlationlength * 4
-
-channel_out_truncated = channel_out[:channel_out.size - (channel_out.size % reshape_width)]
-
-channel_out_reshaped = channel_out_truncated.reshape(channel_out_truncated.size / reshape_width, reshape_width)
-
-channel_autocorr_image = np.zeros((channel_out_reshaped.shape[0], correlationlength))
-
-num_windows = len(channel_out_reshaped)
-
-for i, window in enumerate(channel_out_reshaped):
-    if i % 100 == 0:
-        print("Window {}/{}".format(i, num_windows))
-    channel_autocorr_image[i] = autocorrelate(window, correlationlength)
-
-rows, cols = channel_autocorr_image.shape
-
-print("Shape: {}x{}".format(rows, cols))
-
-aspect_ratio = 1.0
-
-fig = pp.figure()
-ax = fig.add_subplot(111)
-hi = ax.imshow(channel_autocorr_image, cmap='hot', aspect=aspect_ratio*(cols/rows))
-
-fig2 = pp.figure()
-ax = fig2.add_subplot(211)
-accumulated0 = channel_autocorr_image.sum(axis=0)
-ac1 = ax.plot(accumulated0)
-
-ax = fig2.add_subplot(212)
-accumulated1 = channel_autocorr_image.sum(axis=1)
-ac1 = ax.plot(accumulated1)
-
-pp.show()
-