Add hackrf format converter

1 files changed, 39 insertions, 5 deletions

diff --git a/autocorrelate_window.py b/autocorrelate_window.py
index c97f7fb..cf69a77 100755
--- a/autocorrelate_window.py
+++ b/autocorrelate_window.py
@@ -3,17 +3,37 @@
 # 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 = "test.16.14.25.iq"
+file_in = sys.argv[2]
 
-channel_out = np.fromfile(file_in, np.complex64)
+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 = 50
+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)])
@@ -21,23 +41,37 @@ def autocorrelate(x, length):
 
 reshape_width = correlationlength * 4
 
-channel_out_truncated = channel_out[:-(channel_out.size % reshape_width)]
+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))
+        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()