Add first code experiments

1 files changed, 43 insertions, 0 deletions

diff --git a/autocorrelate_window.py b/autocorrelate_window.py
new file mode 100755
index 0000000..c97f7fb
--- /dev/null
+++ b/autocorrelate_window.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+#
+# Do a set of autocorrelations over the test.16.14.25.iq file
+#
+# Licence: see LICENCE file
+
+import numpy as np
+import matplotlib.pyplot as pp
+
+file_in = "test.16.14.25.iq"
+
+channel_out = np.fromfile(file_in, np.complex64)
+
+print("Autocorrelating")
+
+correlationlength = 50
+
+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 % 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))
+for i, window in enumerate(channel_out_reshaped):
+    if i % 100 == 0:
+        print("Window {}".format(i))
+    channel_autocorr_image[i] = autocorrelate(window, correlationlength)
+
+rows, cols = channel_autocorr_image.shape
+
+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))
+
+pp.show()
+