def test_map_africa(self):
# Generate a regular grid of latitude and longitudes with 0.1
# degree resolution for the region of interest.
lat, lon = iturpropag.utils.regular_lat_lon_grid(lat_max=60,
lat_min=-60,
lon_max=65,
lon_min=-35,
resolution_lon=1,
resolution_lat=1)
# Satellite coordinates (GEO, 4 E)
lat_sat = 0
lon_sat = 4
h_sat = 35786 * u.km
# Compute the elevation angle between satellite and ground stations
el = iturpropag.utils.elevation_angle(h_sat, lat_sat, lon_sat, lat,
lon)
# Set the link parameters
f = 22.5 * u.GHz # Link frequency
D = 1.2 * u.m # Antenna diameters
p = 0.1 # Unavailability (Vals exceeded 0.1% of time)
tau = 0 # polarization tilt angle
eta = 0.5 # antenna efficiency
# Compute the atmospheric attenuation
Att = iturpropag.atmospheric_attenuation_slant_path(
lat, lon, f, el, p, D, tau, eta)
# Now we show the surface mean temperature distribution
T = surface_mean_temperature(lat, lon)\
.to(u.Celsius, equivalencies=iturpropag.u.temperature())
# Plot the results
try:
m = iturpropag.utils.plot_in_map(
Att.value,
lat,
lon,
cbar_text='Atmospheric attenuation [dB]',
cmap='magma',
ax=plt.subplot(121))
# Plot the satellite location
m.scatter(lon_sat, lat_sat, c='white', s=20)
# plt.show()
m = iturpropag.utils.plot_in_map(
T.value,
lat,
lon,
cbar_text='Surface mean temperature [C]',
cmap='RdBu_r',
ax=plt.subplot(122))
except RuntimeError as e:
print(e)
def test_single_location_vs_p(self):
# Ground station coordinates (Boston)
lat_GS = 42.3601
lon_GS = -71.0942
# Satellite coordinates (GEO, 77 W)
lat_sat = 0
lon_sat = -77
h_sat = 35786 * u.km
# Compute the elevation angle between satellite and ground station
el = iturpropag.utils.elevation_angle(h_sat, lat_sat, lon_sat, lat_GS,
lon_GS)
f = 22.5 * u.GHz # Link frequency
D = 1.2 * u.m # Antenna diameters
tau = 0 # polarization tilt angle
eta = 0.5 # antenna efficiency
# Define unavailabilities vector in logarithmic scale
# p = np.logspace(-1.5, 1.5, 100)
p = np.logspace(0.05, 0.695, 100)
A_g, A_c, A_r, A_s, A_t = \
iturpropag.atmospheric_attenuation_slant_path(
lat_GS, lon_GS, f, el, p, D, tau, eta, return_contributions=True)
# Plot the results using matplotlib
f, ax = plt.subplots(1, 1)
ax.semilogx(p, A_g.value, label='Gaseous attenuation')
ax.semilogx(p, A_c.value, label='Cloud attenuation')
ax.semilogx(p, A_r.value, label='Rain attenuation')
ax.semilogx(p, A_s.value, label='Scintillation attenuation')
ax.semilogx(p, A_t.value, label='Total atmospheric attenuation')
ax.set_xlabel('Percentage of time attenuation value is exceeded [%]')
ax.set_ylabel('Attenuation [dB]')
ax.grid(which='both', linestyle=':')
plt.legend()
def test_single_location(self):
# Location of the receiver ground stations (Boston, MA)
lat = 42.31
lon = -70.91
print(
'\nThe ITU recommendations predict the following values\nfor the Rx ground station coordinates (Boston, MA)'
)
print('Lat = 42.31, Lon = -70.91')
# Link parameters
el = 60 # Elevation angle equal to 60 degrees
f = 22.5 * u.GHz # Frequency equal to 22.5 GHz
D = 1 * u.m # Receiver antenna diameter of 1 m
p = 0.1 # We compute values exceeded during 0.1 % of
# the average year
eta = 0.6 # Antenna efficiency
tau = 45 # Polarization tilt
print('Elevation angle:\t\t', el, '°')
print('Frequency:\t\t\t', f)
print('Rx antenna diameter:\t\t', D)
print('Values exceeded during 0.1 percent of the year')
print('Antenna efficiency:\t\t', eta)
print('Polarization tilt:\t\t', tau, '°')
# Compute atmospheric parameters
hs = topographic_altitude(lat, lon)
print('Topographic altitude [ITU-R P.1511]:\t', hs)
T = surface_mean_temperature(lat, lon)
print('Surface mean temperature [ITU-R P.1510]:\t', T,
'=', T.value - 273.15, '°C')
P = pressure(lat, hs)
print('Surface pressure [ITU-R P.835]:\t', P)
T_sa = temperature(lat, hs)
print('Standard surface temperature [ITU-R P.835]:\t', T_sa,
'=', T_sa.value - 273.15, '°C')
rho_sa = water_vapour_density(lat, hs)
print('Standard water vapour density [ITU-R P.835]:\t', rho_sa)
rho_p = surface_water_vapour_density(lat, lon, p, hs)
print('Surface water vapour density (p=0.1%) [ITU-R P.836]:\t', rho_p)
V = total_water_vapour_content(lat, lon, p, hs)
print('Total water vapour content (p=0.1%) [ITU-R P.836]:\t', V)
# Compute rain and cloud-related parameters
R_prob = rain_attenuation_probability(lat, lon, el, hs)
print('Rain attenuation probability [ITU-R P.618]:\t', R_prob)
R_pct_prob = rainfall_probability(lat, lon)
print('Rainfall probability [ITU-R P.837]:\t',
R_pct_prob)
R001 = rainfall_rate(lat, lon, p)
print('Rainfall rate exceeded for p=0.1% [ITU-R P.836]:\tt', R001)
h_0 = isotherm_0(lat, lon)
print('0°C Isotherm height [ITU-R P.839]:\t', h_0)
h_rain = rain_height(lat, lon)
print('Rain height [ITU-R P.839]:\t', h_rain)
L_red = columnar_content_reduced_liquid(lat, lon, p)
print('Reduced liquid columnar content (p=0.1%) [P.836]:\t', L_red)
A_w = zenith_water_vapour_attenuation(lat, lon, p, f, h=hs)
print('Zenith water vapour attenuation: [ITU-R P.676]:\t', A_w)
# Compute attenuation values
A_g = gaseous_attenuation_slant_path(f, el, rho_p, P, T)
print('Gaseous attenuation slant path [ITU-R P.676]:\t', A_g)
A_r = rain_attenuation(lat, lon, f, el, p, tau, hs=hs)
print('Rain attenuation [ITU-R P.618]:\t', A_r)
A_c = cloud_attenuation(lat, lon, el, f, p)
print('Cloud attenuation [ITU-R P.840]:\t', A_c)
A_s = scintillation_attenuation(lat, lon, f, el, p, D, eta)
print('Scintillation attenuation [ITU-R P.618]:\t', A_s)
A_t = iturpropag.atmospheric_attenuation_slant_path(
lat, lon, f, el, p, D, tau, eta)
print('Atmospheric attenuation slant path [ITU-R P.618]:\t', A_t)
def test_single_location_vs_f(self):
# Ground station coordinates (Boston)
lat_GS = 42.3601
lon_GS = -71.0942
################################################
# First case: Attenuation vs. frequency #
################################################
# Satellite coordinates (GEO, 77 W)
lat_sat = 0
lon_sat = -77
h_sat = 35786 * u.km
# Compute the elevation angle between satellite and ground station
el = iturpropag.utils.elevation_angle(h_sat, lat_sat, lon_sat, lat_GS,
lon_GS)
f = 22.5 * u.GHz # Link frequency
D = 1.2 * u.m # Antenna diameters
p = 1
tau = 0 # polarization tilt angle
eta = 0.5 # antenna efficiency
f = np.logspace(-0.2, 2, 100) * u.GHz
Ag, Ac, Ar, As, A =\
iturpropag.atmospheric_attenuation_slant_path(lat_GS, lon_GS, f,
el, p, D, tau, eta,
return_contributions=True)
# Plot the results
fig, ax = plt.subplots(1, 1)
ax.loglog(f, Ag, label='Gaseous attenuation')
ax.loglog(f, Ac, label='Cloud attenuation')
ax.loglog(f, Ar, label='Rain attenuation')
ax.loglog(f, As, label='Scintillation attenuation')
ax.loglog(f, A, label='Total atmospheric attenuation')
ax.set_xlabel('Frequency [GHz]')
ax.set_ylabel('Atmospheric attenuation [dB]')
ax.grid(which='both', linestyle=':')
plt.legend(loc='upper left')
################################################
# Second case: Attenuation vs. elevation angle #
################################################
f = 22.5 * u.GHz
el = np.linspace(5, 90, 100)
Ag, Ac, Ar, As, A =\
iturpropag.atmospheric_attenuation_slant_path(lat_GS, lon_GS,
f, el, p, D, tau, eta,
return_contributions=True)
# Plot the results
fig, ax = plt.subplots(1, 1)
ax.plot(el, Ag, label='Gaseous attenuation')
ax.plot(el, Ac, label='Cloud attenuation')
ax.plot(el, Ar, label='Rain attenuation')
ax.plot(el, As, label='Scintillation attenuation')
ax.plot(el, A, label='Total atmospheric attenuation')
ax.set_xlabel('Elevation angle [deg]')
ax.set_ylabel('Atmospheric attenuation [dB]')
ax.grid(which='both', linestyle=':')
plt.legend()
def test_multiple_locations(self):
# Obtain the coordinates of the different cities
# cities = {'Boston': (42.36, -71.06),
# 'New York': (40.71, -74.01),
# 'Los Angeles': (34.05, -118.24),
# 'Denver': (39.74, -104.99),
# 'Las Vegas': (36.20, -115.14),
# 'Seattle': (47.61, -122.33),
# 'Washington DC': (38.91, -77.04)}
cities = { #'Geneva': (46.20, 6.15),
'New York': (40.71, -74.01),
'Nairobi': (-1.28, 36.82),
'Beijing': (39.93, 116.38),
'Moskwa': (55.75, 37.62),
'Buenos Aires': (-34.60, -58.38),
'Sidney': (-33.85, 151.20),
'Cherrapunji': (25.30, 91.70)
}
lat = [coords[0] for coords in cities.values()]
lon = [coords[1] for coords in cities.values()]
# Satellite coordinates (GEO, 4 E)
lat_sat = 0
lon_sat = -77
h_sat = 35786 * u.km
# Compute the elevation angle between satellite and ground stations
el = iturpropag.utils.elevation_angle(h_sat, lat_sat, lon_sat, lat,
lon)
# Set the link parameters
f = 22.5 * u.GHz # Link frequency
D = 1.2 * u.m # Antenna diameters
p = 0.1 # Unavailability (Vals exceeded 0.1% of time)
tau = 0 # polarization tilt angle
eta = 0.5 # antenna efficiency
# Compute the atmospheric attenuation
Ag, Ac, Ar, As, Att = iturpropag.atmospheric_attenuation_slant_path(
lat, lon, f, el, p, D, tau, eta, return_contributions=True)
# Plot the results
city_idx = np.arange(len(cities))
width = 0.15
fig, ax = plt.subplots(1, 1)
ax.bar(city_idx,
Att.value,
width=0.6,
label='Total atmospheric Attenuation')
ax.bar(city_idx - 1.5 * width,
Ar.value,
width=width,
label='Rain attenuation')
ax.bar(city_idx - 0.5 * width,
Ag.value,
width=width,
label='Gaseous attenuation')
ax.bar(city_idx + 0.5 * width,
Ac.value,
width=width,
label='Clouds attenuation')
ax.bar(city_idx + 1.5 * width,
As.value,
width=width,
label='Scintillation attenuation')
# Set the labels
ticks = ax.set_xticklabels([''] + list(cities.keys()))
for t in ticks:
t.set_rotation(45)
ax.set_ylabel('Atmospheric attenuation exceeded for 0.1% [dB]')
# Format image
ax.yaxis.grid(which='both', linestyle=':')
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.3), ncol=2)
plt.tight_layout(rect=(0, 0, 1, 0.85))