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#!/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()
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