118 lines
2.9 KiB
Python
Executable File
118 lines
2.9 KiB
Python
Executable File
import ctypes
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import os.path
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import time
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import numpy as np
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from matplotlib import pyplot as plt
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import data_structures
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def db20(x):
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return 20*np.log10(np.abs(x))
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def db20n(x):
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x = db20(x)
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x = x - np.max(x)
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return x
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def main():
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print('Hello')
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clk = 187.5e6
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# Parse Data
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headers = []
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offset = 0
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file = 'test0.bin'
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fid = open(file, 'rb')
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# Find header, recording buffer could have wrapped depending on data rate and how long we ran for
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hdr_sync = False
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while not hdr_sync:
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# data = recorder.buffer[offset:offset + 4]
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data = fid.read(4)
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sync_word = np.frombuffer(data, dtype=np.uint32)[0]
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if sync_word == 0xAABBCCDD:
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hdr_sync = True
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print('Header found at offset', offset)
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fid.seek(-4, 1)
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# Get the first header
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header = fid.read(ctypes.sizeof(data_structures.CpiHeader))
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header = data_structures.CpiHeader.from_buffer_copy(header)
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fid.seek(-ctypes.sizeof(data_structures.CpiHeader), 1)
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# CPI Parameters (timing values are in clk ticks)
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num_pulses = header.num_pulses
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num_samples = header.num_samples
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pri = header.pri
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inter_cpi = header.inter_cpi
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data_size = num_pulses * num_samples * 4
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file_size = os.path.getsize(file)
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expected_num_cpis = int(file_size / (ctypes.sizeof(data_structures.CpiHeader) + data_size))
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print('File Size', file_size)
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print('Expected CPIS:', expected_num_cpis)
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for i in range(expected_num_cpis):
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# Get Header
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data = fid.read(ctypes.sizeof(data_structures.CpiHeader))
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headers.append(data_structures.CpiHeader.from_buffer_copy(data))
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# Get CPI
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data = fid.read(data_size)
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# Check some header fields
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cpi_times = np.array([x.system_time for x in headers]) / 187.5e6
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pps_frac = np.array([x.pps_frac_sec for x in headers]) / 187.5e6
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pps_sec = np.array([x.pps_sec for x in headers])
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utc_time = pps_sec + pps_frac
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print(pri, inter_cpi, num_pulses * pri + inter_cpi)
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print(cpi_times - cpi_times[0])
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print(pps_frac)
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print(pps_sec - pps_sec[0])
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# Plot last CPI
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data2 = np.frombuffer(data, dtype=np.int16)
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i = data2[0::2]
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q = data2[1::2]
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iq = i + 1j * q
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iq = iq.reshape(-1, num_samples)
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iq = iq + 1e-15
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vmin = -60
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vmax = 0
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# plt.figure()
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# plt.plot(np.diff(cpi_times))
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# plt.plot(np.diff(pps_frac))
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# # plt.ylim([0, .04])
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plt.figure()
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plt.plot(iq[0:4, :].T.real, '.-')
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# plt.plot(iq[0:4, :].T.imag, '--.')
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plt.grid()
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plt.figure()
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plt.plot(np.mean(iq, axis=0).real, '.-')
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plt.plot(np.mean(iq, axis=0).imag, '--.')
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plt.grid()
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plt.figure()
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plt.imshow(db20n(iq), aspect='auto', interpolation='nearest', vmin=vmin, vmax=vmax)
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plt.ylabel('Pulse Count')
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plt.xlabel('Sample Count')
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plt.colorbar()
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plt.show()
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if __name__ == '__main__':
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main()
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