136 lines
3.9 KiB
Python
Executable File
136 lines
3.9 KiB
Python
Executable File
import ctypes
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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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import radar_manager
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from data_recorder import DataRecorder
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# Give 10g eth interface an ip and set MTU for better performance
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# sudo ifconfig enp5s0f0 192.168.2.10 up mtu 5000
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# sudo ifconfig enp5s0f1 192.168.3.10 up mtu 5000
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# Note that increases the size of rmem_max in the linux kernel improves performance for data recording
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# this can be done witht the following terminal command
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# sudo sysctl -w net.core.rmem_max=1048576
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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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# CPI Parameters (timing values are in clk ticks)
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num_pulses = 128
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num_samples = 8192
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start_sample = 0
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tx_num_samples = 1024
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tx_start_sample = start_sample
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pri = int(.001 * clk)
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inter_cpi = 50
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tx_lo_offset = 10e6
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rx_lo_offset = 0
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pri_float = pri / clk
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print('PRI', pri_float, 'PRF', 1 / pri_float)
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print('Expected Data Rate', num_samples * 4 / pri_float / 1e6)
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radar = radar_manager.RadarManager()
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recorder0 = DataRecorder("192.168.2.128", 1234, packet_size=radar.packet_size)
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recorder1 = DataRecorder("192.168.3.128", 1235, packet_size=radar.packet_size)
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recorder0.start_recording('test0.bin', True)
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recorder1.start_recording('test1.bin', True)
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radar.configure_cpi(pri, inter_cpi, num_pulses, num_samples, start_sample,
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tx_num_samples, tx_start_sample, rx_lo_offset, tx_lo_offset)
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print('Start Running')
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radar.start_running()
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# Let it run for a bit
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time.sleep(5)
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# Stop running
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radar.stop_running()
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# Stop the data recorder
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recorder0.stop_recording()
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recorder1.stop_recording()
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# Parse some data
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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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recorders = [recorder0, recorder1]
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for recorder in recorders:
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headers = []
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offset = 0
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plot_recorder = recorder
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hdr_sync = False
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while not hdr_sync:
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data = plot_recorder.buffer[offset:offset + 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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else:
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offset += 4
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num_cpi = 16
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for i in range(num_cpi):
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# Get Header
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data = plot_recorder.buffer[offset:offset + ctypes.sizeof(data_structures.CpiHeader)]
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offset += 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_size = num_pulses * num_samples * 4
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data = plot_recorder.buffer[offset:offset + data_size]
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offset += 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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fid, axs = plt.subplots(2)
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axs[0].plot(iq.T.real, '.-')
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axs[0].plot(iq.T.imag, '--.')
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axs[0].grid()
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axs[1].imshow(db20n(iq), aspect='auto', interpolation='nearest', vmin=vmin, vmax=vmax)
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axs[1].set_ylabel('Pulse Count')
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axs[1].set_xlabel('Sample Count')
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plt.show()
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if __name__ == '__main__':
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main()
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