Remove build and data folders, move tests and utils to the base of the source tree

utils/python/lib/plotKalman.py

@@ -0,0 +1,140 @@
+"""
+ plotKalman.py
+   plotKalman (channelNr, trackResults, settings)
+
+ This function plots the tracking results for the given channel list.
+
+ Irene Pérez Riega, 2023. iperrie@inta.es
+
+      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 doppler 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 plotKalman(channelNr, trackResults, settings):
+
+    # ---------- CHANGE HERE:
+    fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/PlotKalman'
+
+    if not os.path.exists(fig_path):
+        os.makedirs(fig_path)
+
+    # Protection - if the list contains incorrect channel numbers
+    channelNr = np.intersect1d(channelNr,
+                               np.arange(1, settings['numberOfChannels'] + 1))
+
+    for channelNr in channelNr:
+        time_start = settings['timeStartInSeconds']
+        time_axis_in_seconds = np.arange(1, settings['msToProcess']+1)/1000
+
+        # Plot all figures
+        plt.figure(figsize=(1920 / 100, 1080 / 100))
+        plt.clf()
+        plt.gcf().canvas.set_window_title(
+            f'Channel {channelNr} (PRN '
+            f'{str(trackResults[channelNr-1]["PRN"][-2])}) results')
+        plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
+                            hspace=0.4, wspace=0.4)
+        plt.tight_layout()
+
+        # Row 1
+        # ----- CNo for signal -----------------------------------------------
+        # Measure of the ratio between carrier signal power and noise power
+        plt.subplot(4, 2, 1)
+        plt.plot(time_axis_in_seconds,
+                 trackResults[channelNr-1]['CNo'][:settings['msToProcess']],
+                 'b')
+        plt.grid()
+        plt.axis('tight')
+        plt.xlabel('Time (s)')
+        plt.ylabel('CNo (dB-Hz)')
+        plt.title('Carrier to Noise Ratio', fontweight='bold')
+
+        # ----- PLL discriminator filtered -----------------------------------
+        plt.subplot(4, 2, 2)
+        plt.plot(time_axis_in_seconds,
+                 trackResults[channelNr-1]['state1']
+                 [:settings['msToProcess']], 'b')
+        plt.grid()
+        plt.axis('tight')
+        plt.xlim([time_start, time_axis_in_seconds[-1]])
+        plt.xlabel('Time (s)')
+        plt.ylabel('Phase Amplitude')
+        plt.title('Filtered Carrier Phase', fontweight='bold')
+
+        # Row 2
+        # ----- Carrier Frequency --------------------------------------------
+        # Filtered carrier frequency of (transmitted by a satellite)
+        # for a specific channel
+        plt.subplot(4, 2, 3)
+        plt.plot(time_axis_in_seconds[1:],
+                 trackResults[channelNr-1]['state2']
+                 [1:settings['msToProcess']], color=[0.42, 0.25, 0.39])
+        plt.grid()
+        plt.axis('auto')
+        plt.xlim(time_start, time_axis_in_seconds[-1])
+        plt.xlabel('Time (s)')
+        plt.ylabel('Freq (Hz)')
+        plt.title('Filtered Carrier Frequency', fontweight='bold')
+
+        # ----- Carrier Frequency Rate ---------------------------------------
+        plt.subplot(4, 2, 4)
+        plt.plot(time_axis_in_seconds[1:],
+                 trackResults[channelNr-1]['state3']
+                 [1:settings['msToProcess']], color=[0.42, 0.25, 0.39])
+        plt.grid()
+        plt.axis('auto')
+        plt.xlim(time_start, time_axis_in_seconds[-1])
+        plt.xlabel('Time (s)')
+        plt.ylabel('Freq (Hz)')
+        plt.title('Filtered Carrier Frequency Rate', fontweight='bold')
+
+        # Row 3
+        # ----- PLL discriminator unfiltered----------------------------------
+        plt.subplot(4, 2, (5,6))
+        plt.plot(time_axis_in_seconds,
+                 trackResults[channelNr-1]['innovation'], 'r')
+        plt.grid()
+        plt.axis('auto')
+        plt.xlim(time_start, time_axis_in_seconds[-1])
+        plt.xlabel('Time (s)')
+        plt.ylabel('Amplitude')
+        plt.title('Raw PLL discriminator (Innovation)',fontweight='bold')
+
+        # Row 4
+        # ----- PLL discriminator covariance ---------------------------------
+        plt.subplot(4, 2, (7,8))
+        plt.plot(time_axis_in_seconds,
+                 trackResults[channelNr-1]['r_noise_cov'], 'r')
+        plt.grid()
+        plt.axis('auto')
+        plt.xlim(time_start, time_axis_in_seconds[-1])
+        plt.xlabel('Time (s)')
+        plt.ylabel('Variance')
+        plt.title('Estimated Noise Variance', fontweight='bold')
+
+        plt.tight_layout()
+        plt.savefig(os.path.join(fig_path,
+                                 f'kalman_ch{channelNr}_PRN_'
+                                 f'{trackResults[channelNr - 1]["PRN"][-1]}'
+                                 f'.png'))
+        plt.show()