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"""
plotVEMLTracking.py
This function plots the tracking results for the given channel list.
Irene Pérez Riega, 2023. iperrie@inta.es
plotVEMLTracking(channelNr, trackResults, settings)
Args:
channelList - list of channels to be plotted.
trackResults - tracking results from the tracking function.
settings - receiver settings.
Modifiable in the file:
fig_path - Path where plots will be save
-----------------------------------------------------------------------------
GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
This file is part of GNSS-SDR.
Copyright (C) 2022 (see AUTHORS file for a list of contributors)
SPDX-License-Identifier: GPL-3.0-or-later
-----------------------------------------------------------------------------
"""
import matplotlib.pyplot as plt
import numpy as np
import os
def plotVEMLTracking(channelNr, trackResults, settings):
# ---------- CHANGE HERE:
fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/VEMLTracking'
if not os.path.exists(fig_path):
os.makedirs(fig_path)
# Protection - if the list contains incorrect channel numbers
if channelNr in list(range(1,settings["numberOfChannels"]+1)):
plt.figure(figsize=(1920 / 120, 1080 / 120))
plt.clf()
plt.gcf().canvas.set_window_title(
f'Channel {channelNr} (PRN '
f'{trackResults[channelNr-1]["PRN"][0]}) results')
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
hspace=0.4, wspace=0.4)
# Extract timeAxis and time_label
if 'prn_start_time_s' in trackResults[channelNr-1]:
timeAxis = trackResults[channelNr-1]['prn_start_time_s']
time_label = 'RX Time (s)'
else:
timeAxis = np.arange(1, len(trackResults[channelNr-1]['PRN']) + 1)
time_label = 'Epoch'
len_dataI = len (trackResults[channelNr-1]["data_I"])
len_dataQ = len (trackResults[channelNr-1]["data_Q"])
if len_dataI < len_dataQ:
dif = len_dataQ - len_dataI
trackResults[channelNr-1]["data_I"] = np.pad(
trackResults[channelNr-1]["data_I"], pad_width=(0,dif),
mode="constant", constant_values=0)
elif len_dataQ < len_dataI:
dif = len_dataI - len_dataQ
trackResults[channelNr-1]["data_Q"] = np.pad(
trackResults[channelNr-1]["data_Q"], pad_width=(0,dif),
mode="constant", constant_values=0 )
# Discrete-Time Scatter Plot
plt.subplot(3, 3, 1)
plt.plot(trackResults[channelNr-1]['data_I'],
trackResults[channelNr-1]['data_Q'], marker='.',
markersize=1, linestyle=' ')
plt.grid()
plt.axis('equal')
plt.title('Discrete-Time Scatter Plot', fontweight='bold')
plt.xlabel('I prompt')
plt.ylabel('Q prompt')
# Nav bits
plt.subplot(3, 3, (2, 3))
plt.plot(timeAxis, trackResults[channelNr-1]['data_I'],
linewidth=1)
plt.grid()
plt.title('Bits of the navigation message', fontweight='bold')
plt.xlabel(time_label)
plt.axis('tight')
# Raw PLL discriminator unfiltered
plt.subplot(3, 3, 4)
plt.plot(timeAxis, trackResults[channelNr-1]['pllDiscr'],
color='r', linewidth=1)
plt.grid()
plt.axis('tight')
plt.xlabel(time_label)
plt.ylabel('Amplitude')
plt.title('Raw PLL discriminator', fontweight='bold')
# Correlation results
plt.subplot(3, 3, (5, 6))
corr_data = [
np.sqrt(trackResults[channelNr-1]['I_VE'] ** 2 +
trackResults[channelNr-1]['Q_VE'] ** 2),
np.sqrt(trackResults[channelNr-1]['I_E'] ** 2 +
trackResults[channelNr-1]['Q_E'] ** 2),
np.sqrt(trackResults[channelNr-1]['I_P'] ** 2 +
trackResults[channelNr-1]['Q_P'] ** 2),
np.sqrt(trackResults[channelNr-1]['I_L'] ** 2 +
trackResults[channelNr-1]['Q_L'] ** 2),
np.sqrt(trackResults[channelNr-1]['I_VL'] ** 2 +
trackResults[channelNr-1]['Q_VL'] ** 2)
]
line = []
colors = ['b','#FF6600','#FFD700','purple','g']
for i, data in enumerate(corr_data):
line.append(plt.plot(timeAxis, data, label=f'Data {i+1}',
color=colors[i], marker='*', linestyle=' ',
linewidth=1))
plt.grid()
plt.title('Correlation results',fontweight='bold')
plt.xlabel(time_label)
plt.axis('tight')
plt.legend([r'$\sqrt{I_{VE}^2 + Q_{VE}^2}$',
r'$\sqrt{I_{E}^2 + Q_{E}^2}$',
r'$\sqrt{I_{P}^2 + Q_{P}^2}$',
r'$\sqrt{I_{L}^2 + Q_{L}^2}$',
r'$\sqrt{I_{VL}^2 + Q_{VL}^2}$'], loc='best')
# Filtered PLL discriminator
plt.subplot(3, 3, 7)
plt.plot(timeAxis, trackResults[channelNr-1]['pllDiscrFilt'],
'b', linewidth=1)
plt.grid()
plt.axis('tight')
plt.xlabel(time_label)
plt.ylabel('Amplitude')
plt.title('Filtered PLL discriminator', fontweight='bold')
# Raw DLL discriminator unfiltered
plt.subplot(3, 3, 8)
plt.plot(timeAxis, trackResults[channelNr-1]['dllDiscr'], 'r',
linewidth=1)
plt.grid()
plt.axis('tight')
plt.xlabel(time_label)
plt.ylabel('Amplitude')
plt.title('Raw DLL discriminator',fontweight='bold')
# Filtered DLL discriminator
plt.subplot(3, 3, 9)
plt.plot(timeAxis, trackResults[channelNr-1]['dllDiscrFilt'],
'b', linewidth=1)
plt.grid()
plt.axis('tight')
plt.xlabel(time_label)
plt.ylabel('Amplitude')
plt.title('Filtered DLL discriminator',fontweight='bold')
plt.savefig(os.path.join(fig_path,
f'Ch{channelNr}_PRN'
f'{trackResults[channelNr-1]["PRN"][0]}'
f'_results'))
plt.show()
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