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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
This commit is contained in:
Carles Fernandez
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"""
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plotVEMLTracking.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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plotVEMLTracking(channelNr, 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 matplotlib.pyplot as plt
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import numpy as np
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import os
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def plotVEMLTracking(channelNr, trackResults, settings):
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# ---------- CHANGE HERE:
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fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/VEMLTracking'
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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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# 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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len_dataI = len (trackResults[channelNr-1]["data_I"])
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len_dataQ = len (trackResults[channelNr-1]["data_Q"])
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if len_dataI < len_dataQ:
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dif = len_dataQ - len_dataI
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trackResults[channelNr-1]["data_I"] = np.pad(
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trackResults[channelNr-1]["data_I"], pad_width=(0,dif),
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mode="constant", constant_values=0)
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elif len_dataQ < len_dataI:
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dif = len_dataI - len_dataQ
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trackResults[channelNr-1]["data_Q"] = np.pad(
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trackResults[channelNr-1]["data_Q"], pad_width=(0,dif),
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mode="constant", constant_values=0 )
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# Discrete-Time Scatter Plot
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plt.subplot(3, 3, 1)
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plt.plot(trackResults[channelNr-1]['data_I'],
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trackResults[channelNr-1]['data_Q'], marker='.',
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markersize=1, 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(3, 3, (2, 3))
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plt.plot(timeAxis, trackResults[channelNr-1]['data_I'],
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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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# Raw PLL discriminator unfiltered
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plt.subplot(3, 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(3, 3, (5, 6))
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corr_data = [
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np.sqrt(trackResults[channelNr-1]['I_VE'] ** 2 +
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trackResults[channelNr-1]['Q_VE'] ** 2),
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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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np.sqrt(trackResults[channelNr-1]['I_VL'] ** 2 +
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trackResults[channelNr-1]['Q_VL'] ** 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, label=f'Data {i+1}',
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color=colors[i], marker='*', linestyle=' ',
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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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# Filtered PLL discriminator
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plt.subplot(3, 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(3, 3, 8)
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plt.plot(timeAxis, trackResults[channelNr-1]['dllDiscr'], 'r',
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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(3, 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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plt.savefig(os.path.join(fig_path,
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f'Ch{channelNr}_PRN'
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f'{trackResults[channelNr-1]["PRN"][0]}'
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f'_results'))
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plt.show()
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