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"""
plotPosition.py
plot_position(navSolutions)
Graph Latitude-Longitude and X-Y-X as a function of Transmit Time
Args:
navSolutions - A dictionary with the processed information in lists
plot_oneVStime(navSolutions, name)
Graph of a variable as a function of transmission time
Args:
navSolutions - A dictionary with the processed information in lists
name - navSolutions variable name that we want to plot
calcularCEFP(percentil, navSolutions, m_lat, m_long)
Calculate CEFP radio [m] for n percentil.
Args:
percentil - Number of measures that will be inside the circumference
navSolutions - A dictionary with the processed information in lists
m_lat - Mean latitude measures [º]
m_long - Mean longitude measures [º]
Modifiable in the file:
fig_path - Path where plots will be save
fig_path_maps - Path where the maps will be save
filename_map - Path where map will be save
filename_map_t - Path where terrain map will be save
Irene Pérez Riega, 2023. iperrie@inta.es
-----------------------------------------------------------------------------
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 math
import os.path
import webbrowser
import numpy as np
import matplotlib.pyplot as plt
import folium
def plot_position(navSolutions):
# ---------- CHANGE HERE:
fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/PlotPosition/'
fig_path_maps = fig_path + 'maps/'
filename_map = 'mapPlotPosition.html'
filename_map_t = 'mapTerrainPotPosition.html'
if not os.path.exists(fig_path_maps):
os.mkdir(fig_path_maps)
# Statics Positions:
m_lat = sum(navSolutions['latitude']) / len(navSolutions['latitude'])
m_long = sum(navSolutions['longitude']) / len(navSolutions['longitude'])
# CEFP_n -> Include the n% of the dots in the circle
r_CEFP_95 = calcularCEFP(95, navSolutions, m_lat, m_long)
r_CEFP_50 = calcularCEFP(50, navSolutions, m_lat, m_long)
# Generate and save html with the positions
m = folium.Map(location=[navSolutions['latitude'][0],
navSolutions['longitude'][0]], zoom_start=100)
c_CEFP95 = folium.Circle(location=[m_lat, m_long],
radius=r_CEFP_95, color='green', fill=True,
fill_color='green', fill_opacity=0.5)
c_CEFP50 = folium.Circle(location=[m_lat, m_long], radius=r_CEFP_50,
color='red', fill=True, fill_color='red',
fill_opacity=0.5)
# POP-UPs
popup95 = folium.Popup("(Green)CEFP95 diameter: {} "
"metres".format(2 * r_CEFP_95))
popup95.add_to(c_CEFP95)
popup50 = folium.Popup("(Red)CEFP50 diameter: {} "
"metres".format(2 * r_CEFP_50))
popup50.add_to(c_CEFP50)
c_CEFP95.add_to(m)
c_CEFP50.add_to(m)
# Optional: Plot each point ->
"""
for i in range(len(navSolutions['latitude'])):
folium.Marker(location=[navSolutions['latitude'][i],
navSolutions['longitude'][i]],
icon=folium.Icon(color='red')).add_to(m)
"""
m.save(fig_path_maps + filename_map)
webbrowser.open(fig_path_maps + filename_map)
# Optional: with terrain ->
"""
n = folium.Map(location=[navSolutions['latitude'][0],
navSolutions['longitude'][0]], zoom_start=100,
tiles='Stamen Terrain')
c_CEFP95.add_to(n)
c_CEFP50.add_to(n)
n.save(fig_path_maps + filename_map_t)
webbrowser.open(fig_path_maps + filename_map_t)
"""
# Plot ->
time = []
for i in range(len(navSolutions['TransmitTime'])):
time.append(round(navSolutions['TransmitTime'][i] -
min(navSolutions['TransmitTime']), 3))
plt.figure(figsize=(1920 / 120, 1080 / 120))
plt.clf()
plt.suptitle(f'Plot file PVT process data results')
# Latitude and Longitude
plt.subplot(1, 2, 1)
scatter = plt.scatter(navSolutions['latitude'], navSolutions['longitude'],
c=time, marker='.')
plt.grid()
plt.ticklabel_format(style='plain', axis='both', useOffset=False)
plt.title('Positions latitud-longitud')
plt.xlabel('Latitude º')
plt.ylabel('Longitude º')
plt.axis('tight')
# Colors
cmap = plt.get_cmap('viridis')
norm = plt.Normalize(vmin=min(time), vmax=max(time))
scatter.set_cmap(cmap)
scatter.set_norm(norm)
colors = plt.colorbar(scatter)
colors.set_label('TransmitTime [s]')
# X, Y, Z
ax = plt.subplot(1, 2, 2, projection='3d')
plt.ticklabel_format(style='plain', axis='both', useOffset=False)
ax.scatter(navSolutions['X'], navSolutions['Y'], navSolutions['Z'],
c=time, marker='.')
ax.set_xlabel('Eje X [m]')
ax.set_ylabel('Eje Y [m]')
ax.set_zlabel('Eje Z [m]')
ax.set_title('Positions x-y-z')
plt.tight_layout()
plt.savefig(os.path.join(fig_path, f'PVT_ProcessDataResults.png'))
plt.show()
def plot_oneVStime(navSolutions, name):
# ---------- CHANGE HERE:
fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/PlotPosition/'
if not os.path.exists(fig_path):
os.mkdir(fig_path)
time = []
for i in range(len(navSolutions['TransmitTime'])):
time.append(round(navSolutions['TransmitTime'][i] -
min(navSolutions['TransmitTime']), 3))
plt.clf()
plt.scatter(time, navSolutions[name], marker='.')
plt.grid()
plt.title(f'{name} vs Time')
plt.xlabel('Time [s]')
plt.ylabel(name)
plt.axis('tight')
plt.ticklabel_format(style='plain', axis='both', useOffset=False)
plt.tight_layout()
plt.savefig(os.path.join(fig_path, f'{name}VSTime.png'))
plt.show()
def calcularCEFP(percentil, navSolutions, m_lat, m_long):
r_earth = 6371000
lat = []
long = []
dlat = []
dlong = []
dist = []
m_lat = math.radians(m_lat)
m_long = math.radians(m_long)
for i in range(len(navSolutions['latitude'])):
lat.append(math.radians(navSolutions['latitude'][i]))
long.append(math.radians(navSolutions['longitude'][i]))
for i in range(len(lat)):
dlat.append(m_lat - lat[i])
dlong.append(m_long - long[i])
# Haversine:
a = (math.sin(dlat[i] / 2) ** 2 +
math.cos(lat[i]) * math.cos(m_lat) * math.sin(dlong[i] / 2) ** 2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
dist.append(r_earth * c)
# Radio CEFP
radio_CEFP_p = np.percentile(dist, percentil)
return radio_CEFP_p
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