Calculate path propagation/attenuation according to ITU-R P.452 (16)¶

License¶

Calculate path propagation/attenuation according to ITU-R P.452 (16).
Copyright (C) 2015+  Benjamin Winkel (bwinkel@mpifr.de)

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

In [1]:

%matplotlib inline

In [2]:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation, rc
from astropy import units as u
from pycraf import pathprof
from pycraf import conversions as cnv

rc('animation', html='html5')
FRAMES = 120

Influence of the free parameters¶

The propagation losses depend on a lot of different parameters, such as frequency, distance, weather etc. Here, we try to visualize some of the effects. The generic (flat-Earth) case is used (without loss of generality).

In [3]:

distance = 100 * u.km
hprof_step = 50 * u.m
lon_t, lat_t = 0 * u.deg, 50 * u.deg

In [4]:

hprof_data = pathprof.height_path_data_generic(
    distance, hprof_step, lon_t, lat_t,
    )

In [5]:

def plot_helper(freq, temp, press, h_tg, h_rg, time_percent, titles):
    
    plt.close()
    fig = plt.figure(figsize=(14, 10))
    ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))

    lines_dict = {}
    for key, name, style in zip(
            ['L_b0p', 'L_bd', 'L_bs', 'L_ba', 'L_b'],
            ['LOS', 'Diffraction', 'Troposcatter', 'Ducting', 'Total'], 
            ['b-', 'r-', 'c-', 'g-', 'k--']
            ):
        results = pathprof.atten_path_fast(
            freq[0], temp[0], press[0],
            h_tg[0], h_rg[0], time_percent[0],
            hprof_data,
            )
        lines_dict[key] = ax.plot(
            hprof_data['distances'][5:],
            results[key][5:],
            style, label=name
            )[0]

    ax.legend(
        *ax.get_legend_handles_labels(), 
        loc='lower right', fontsize=8, handlelength=3
        )
    title = ax.set_title(titles[0], loc='center', fontsize=20)

    ax.set_xlim((0, 100))
    ax.set_ylim((80, 260))
    ax.set_xlabel('Distance [km]')
    ax.set_ylabel('Path attenuation [dB]')
    ax.grid()


    def animate_freq(i):

        results = pathprof.atten_path_fast(
            freq[i], temp[i], press[i],
            h_tg[i], h_rg[i], time_percent[i],
            hprof_data,
            )

        title.set_text(titles[i])
        for key in ['L_b0p', 'L_bd', 'L_bs', 'L_ba', 'L_b']:

            lines_dict[key].set_ydata(results[key][5:])

        return list(lines_dict.values())

    def init():
        return animate_freq(0)

    # call the animator. blit=True means only re-draw the parts that have changed.
    anim = animation.FuncAnimation(
        fig, animate_freq, init_func=init, frames=FRAMES, interval=50, blit=True
        )

    # this takes a while!
    plt.close(anim._fig)

    return anim

Frequency dependence¶

In [6]:

h_tg, h_rg = np.full(FRAMES, 30) * u.m, np.full(FRAMES, 30) * u.m
freq = np.logspace(np.log10(0.1), np.log10(50), FRAMES) * u.GHz
temp = np.full(FRAMES, 293.15) * u.K
press = np.full(FRAMES, 1013.) * u.hPa

time_percent = np.full(FRAMES, 2) * u.percent

titles = ['Frequency: {:5.2f}'.format(f) for f in freq]

In [7]:

plot_helper(freq, temp, press, h_tg, h_rg, time_percent, titles)

Out[7]:

Temperature dependence¶

In [8]:

h_tg, h_rg = np.full(FRAMES, 30) * u.m, np.full(FRAMES, 30) * u.m
freq = np.full(FRAMES, 10.) * u.GHz
temp = np.linspace(250., 330, FRAMES) * u.K
press = np.full(FRAMES, 1013.) * u.hPa

time_percent = np.full(FRAMES, 2) * u.percent

titles = ['Temperature: {:5.2f}'.format(t) for t in temp]

In [9]:

plot_helper(freq, temp, press, h_tg, h_rg, time_percent, titles)

Out[9]:

Time-percent dependence¶

In [10]:

h_tg, h_rg = np.full(FRAMES, 30) * u.m, np.full(FRAMES, 30) * u.m
freq = np.full(FRAMES, 10.) * u.GHz
temp = np.full(FRAMES, 293.15) * u.K
press = np.full(FRAMES, 1013.) * u.hPa

time_percent = np.logspace(np.log10(0.1), np.log10(50), FRAMES) * u.percent

titles = ['Time percent: {:5.2f}'.format(p) for p in time_percent]

In [11]:

plot_helper(freq, temp, press, h_tg, h_rg, time_percent, titles)

Out[11]:

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