atten_path_fast#

pycraf.pathprof.atten_path_fast(freq, temperature, pressure, h_tg, h_rg, timepercent, hprof_data, polarization=0, version=16, base_water_density=<Quantity 7.5 g / m3>)[source]#

Calculate attenuation along a path using a parallelized method.

Parameters:

freqQuantity: Frequency of radiation [GHz]
temperatureQuantity: Temperature (K)
pressureQuantity: Pressure (hPa)
h_tg, h_rgQuantity: Transmitter/receiver heights over ground [m]
timepercentQuantity: Time percentage [%] (maximal 50%)
hprof_datadict, dict-like: Dictionary with height profile auxillary data as calculated with height_path_data or height_path_data_generic.
polarizationint, optional: Polarization (default: 0) Allowed values are: 0 - horizontal, 1 - vertical
versionint, optional: ITU-R Rec. P.452 version. Allowed values are: 14, 16
base_water_densityQuantity, optional: For atmospheric attenuation, the water vapor content plays a role. In Rec. ITU-R P.452, Eq. (9a), the water content is variable (depending on the fraction of the path over the water). However, the base level is set to \(7.5~\mathrm{g}/\mathrm{m}^3\). For extraordinarily dry places, which are often used for radio astronomy, this value can be too high. (Default: 7.5 g / m^3)

Returns:

resultsdict

Results of the path attenuation calculation. Each entry in the dictionary is a 1D ndarray containing the associated value for the path. The following entries are contained:

L_b0p - Free-space loss including focussing effects
(for p% of time) [dB]
L_bd - Basic transmission loss associated with diffraction
not exceeded for p% time [dB]; L_bd = L_b0p + L_dp
L_bs - Tropospheric scatter loss [dB]
L_ba - Ducting/layer reflection loss [dB]
L_b - Complete path propagation loss [dB]
L_b_corr - As L_b but with clutter correction [dB]
eps_pt - Elevation angle of paths w.r.t. Tx [deg]
eps_pr - Elevation angle of paths w.r.t. Rx [deg]
d_lt - Distance to horizon w.r.t. Tx [km]
d_lr - Distance to horizon w.r.t. Rx [km]
path_type - Path type (0 - LoS, 1 - Trans-horizon)

Notes

The diffraction-loss algorithm was changed between ITU-R P.452 version 14 and 15. The former used a Deygout method, the new one is based on a Bullington calculation with correction terms.
In future versions, more entries may be added to the results dictionary.

Examples

A typical usage would be:

import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
from pycraf import pathprof

lon_t, lat_t = 6.8836 * u.deg, 50.525 * u.deg
lon_r, lat_r = 7.3334 * u.deg, 50.635 * u.deg
hprof_step = 100 * u.m

hprof_data = pathprof.height_path_data(
    lon_t, lat_t, lon_r, lat_r, hprof_step,
    zone_t=pathprof.CLUTTER.URBAN, zone_r=pathprof.CLUTTER.SUBURBAN,
    )

plt.plot(hprof_data['distances'], hprof_data['heights'], 'k-')
plt.grid()
plt.show()

freq = 1. * u.GHz
temperature = 290. * u.K
pressure = 1013. * u.hPa
h_tg, h_rg = 5. * u.m, 50. * u.m
time_percent = 2. * u.percent

results = pathprof.atten_path_fast(
    freq, temperature, pressure,
    h_tg, h_rg, time_percent,
    hprof_data,
    )

for k in ['L_bfsg', 'L_bd', 'L_bs', 'L_ba', 'L_b', 'L_b_corr']:
    plt.plot(hprof_data['distances'], results[k], '-')

plt.ylim((50, 275))
plt.grid()
plt.legend(
    ['LOS', 'Diffraction', 'Troposcatter', 'Ducting',
    'Total', 'Total w. clutter'
    ], fontsize=10, loc='lower right')
plt.show()

plt.plot(hprof_data['distances'], eps_pt_path, 'b-')
plt.plot(hprof_data['distances'], eps_pr_path, 'r-')
plt.grid()
plt.show()