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https://github.com/gnss-sdr/gnss-sdr
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Remove build and data folders, move tests and utils to the base of the source tree
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Carles Fernandez
190
utils/python/lib/plotTracking.py
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190
utils/python/lib/plotTracking.py
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"""
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plotTracking.py
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This function plots the tracking results for the given channel list.
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Irene Pérez Riega, 2023. iperrie@inta.es
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plotTracking(channelList, trackResults, settings)
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Args:
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channelList - list of channels to be plotted.
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trackResults - tracking results from the tracking function.
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settings - receiver settings.
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Modifiable in the file:
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fig_path - Path where plots will be save
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-----------------------------------------------------------------------------
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GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
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This file is part of GNSS-SDR.
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Copyright (C) 2022 (see AUTHORS file for a list of contributors)
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SPDX-License-Identifier: GPL-3.0-or-later
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-----------------------------------------------------------------------------
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"""
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import numpy as np
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import os
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import matplotlib.pyplot as plt
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def plotTracking(channelNr, trackResults, settings):
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# ---------- CHANGE HERE:
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fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/PlotTracking'
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if not os.path.exists(fig_path):
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os.makedirs(fig_path)
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# Protection - if the list contains incorrect channel numbers
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if channelNr in list(range(1,settings["numberOfChannels"]+1)):
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plt.figure(figsize=(1920 / 120, 1080 / 120))
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plt.clf()
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plt.gcf().canvas.set_window_title(
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f'Channel {channelNr} (PRN '
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f'{trackResults[channelNr-1]["PRN"][0]}) results')
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plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
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hspace=0.4, wspace=0.4)
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plt.tight_layout()
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# Extract timeAxis and time_label
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if 'prn_start_time_s' in trackResults[channelNr-1]:
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timeAxis = trackResults[channelNr-1]['prn_start_time_s']
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time_label = 'RX Time (s)'
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else:
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timeAxis = np.arange(1, len(trackResults[channelNr-1]['PRN']) + 1)
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time_label = 'Epoch'
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# Row 1 ==============================================================
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# Discrete-Time Scatter Plot
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plt.subplot(4, 3, 1)
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plt.plot(trackResults[channelNr-1]['I_P'],
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trackResults[channelNr-1]['Q_P'], marker='.', markersize=1,
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linestyle=' ')
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plt.grid()
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plt.axis('equal')
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plt.title('Discrete-Time Scatter Plot', fontweight='bold')
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plt.xlabel('I prompt')
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plt.ylabel('Q prompt')
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# Nav bits
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plt.subplot(4, 3, (2, 3))
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plt.plot(timeAxis, trackResults[channelNr-1]['I_P'], linewidth=1)
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plt.grid()
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plt.title('Bits of the navigation message', fontweight='bold')
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plt.xlabel(time_label)
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plt.axis('tight')
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# Row 2 ==============================================================
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# Raw PLL discriminator unfiltered
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plt.subplot(4, 3, 4)
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plt.plot(timeAxis, trackResults[channelNr-1]['pllDiscr'],
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color='r', linewidth=1)
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plt.grid()
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plt.axis('tight')
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plt.xlabel(time_label)
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plt.ylabel('Amplitude')
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plt.title('Raw PLL discriminator', fontweight='bold')
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# Correlation results
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plt.subplot(4, 3, (5, 6))
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corr_data = [
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np.sqrt(trackResults[channelNr-1]['I_E'] ** 2 +
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trackResults[channelNr-1]['Q_E'] ** 2),
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np.sqrt(trackResults[channelNr-1]['I_P'] ** 2 +
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trackResults[channelNr-1]['Q_P'] ** 2),
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np.sqrt(trackResults[channelNr-1]['I_L'] ** 2 +
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trackResults[channelNr - 1]['Q_L'] ** 2)
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]
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line = []
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colors = ['b', '#FF6600', '#FFD700', 'purple', 'g']
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for i, data in enumerate(corr_data):
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line.append(plt.plot(timeAxis, data,
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label=f'Data {i+1}', color=colors[i],
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marker='*', linestyle=' ', linewidth=1))
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plt.grid()
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plt.title('Correlation results', fontweight='bold')
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plt.xlabel(time_label)
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plt.axis('tight')
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plt.legend([r'$\sqrt{I_{VE}^2 + Q_{VE}^2}$',
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r'$\sqrt{I_{E}^2 + Q_{E}^2}$',
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r'$\sqrt{I_{P}^2 + Q_{P}^2}$',
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r'$\sqrt{I_{L}^2 + Q_{L}^2}$',
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r'$\sqrt{I_{VL}^2 + Q_{VL}^2}$'], loc='best')
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# Row 3 ==============================================================
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# Filtered PLL discriminator
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plt.subplot(4, 3, 7)
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plt.plot(timeAxis, trackResults[channelNr-1]['pllDiscrFilt'],
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'b', linewidth=1)
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plt.grid()
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plt.axis('tight')
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plt.xlabel(time_label)
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plt.ylabel('Amplitude')
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plt.title('Filtered PLL discriminator', fontweight='bold')
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# Raw DLL discriminator unfiltered
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plt.subplot(4, 3, 8)
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plt.plot(timeAxis, trackResults[channelNr-1]['dllDiscr'],
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'r', linewidth=1)
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plt.grid()
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plt.axis('tight')
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plt.xlabel(time_label)
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plt.ylabel('Amplitude')
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plt.title('Raw DLL discriminator', fontweight='bold')
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# Filtered DLL discriminator
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plt.subplot(4, 3, 9)
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plt.plot(timeAxis, trackResults[channelNr-1]['dllDiscrFilt'],
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'b', linewidth=1)
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plt.grid()
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plt.axis('tight')
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plt.xlabel(time_label)
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plt.ylabel('Amplitude')
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plt.title('Filtered DLL discriminator', fontweight='bold')
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# Row 4 ==============================================================
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# CNo for signal
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plt.subplot(4, 3, 10)
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plt.plot(timeAxis, trackResults[channelNr-1]['CNo'], 'b',
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linewidth=1)
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plt.grid()
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plt.axis('equal')
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plt.xlabel('Time (s)')
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plt.ylabel('CNo (dB-Hz)')
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plt.title('Carrier to Noise Ratio', fontweight='bold')
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# Carrier Frequency
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plt.subplot(4, 3, 11)
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plt.plot(timeAxis, trackResults[channelNr-1]['carrFreq'],
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marker='.', markersize=1, linestyle=' ')
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plt.grid()
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plt.axis('equal')
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plt.xlabel('Time (s)')
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plt.ylabel('Freq (hz)')
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plt.title('Carrier Frequency', fontweight='bold')
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# Code Frequency
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# Skip sample 0 to help with results display
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plt.subplot(4, 3, 12)
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plt.plot(timeAxis, trackResults[channelNr-1]['codeFreq'],
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marker='.', markersize=1, linestyle=' ')
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plt.grid()
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plt.axis('equal')
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plt.xlabel('Time (s)')
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plt.ylabel('Freq (Hz)')
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plt.title('Code Frequency',fontweight='bold')
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plt.tight_layout()
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plt.savefig(os.path.join(fig_path,
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f'trk_dump_ch{channelNr}_PRN_'
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f'{trackResults[channelNr - 1]["PRN"][-1]}'
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f'.png'))
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plt.show()
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