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已同步 2025-08-06 04:26:52 +08:00
Added antenna patterns module.
这个提交包含在：
Craig Warren
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# Copyright (C) 2016, Craig Warren
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#
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# This module is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
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# To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/.
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#
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# Please use the attribution at http://dx.doi.org/10.1016/j.sigpro.2016.04.010
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import argparse
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import os
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import h5py
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import numpy as np
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import matplotlib.pyplot as plt
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from gprMax.constants import c, z0
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='Calculate and store (in a Numpy file) field patterns from a simulation with receivers positioned in circles around an antenna.', usage='cd gprMax; python -m user_libs.antenna_patterns.initial_save outputfile')
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parser.add_argument('outputfile', help='name of gprMax output file including path')
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args = parser.parse_args()
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outputfile = args.outputfile
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########################################
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# User configurable parameters
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# Pattern type (E or H)
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type = 'H'
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# Antenna (true if using full antenna model; false for a theoretical Hertzian dipole
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antenna = True
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# Relative permittivity of half-space for homogeneous materials (set to None for inhomogeneous)
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epsr = 5
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# Observation radii and angles
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radii = np.linspace(0.1, 0.3, 20)
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theta = np.linspace(3, 357, 60) * (180/np.pi)
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# Scaling of time-domain field pattern values by material impedance
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impscaling = False
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# Centre frequency of modelled antenna
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f = 1.5e9 # GSSI 1.5GHz antenna model
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# Largest dimension of antenna transmitting element
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D = 0.060 # GSSI 1.5GHz antenna model
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# Traces to plot for sanity checking
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traceno = np.s_[:] # All traces
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########################################
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# Critical angle and velocity
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if epsr:
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mr = 1
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z1 = np.sqrt(mr/epsr) * z0
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v1 = c / np.sqrt(epsr)
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thetac = np.round(np.arcsin(v1/c) * (180/np.pi))
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wavelength = v1/f
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# Print some useful information
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print('Centre frequency: {} GHz'.format(f/1e9))
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if epsr:
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print('Critical angle for Er {} is {} degrees'.format(epsr, thetac))
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print('Wavelength: {:.3f} m'.format(wavelength))
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print('Observation distance(s) from {:.3f} m ({:.1f} wavelengths) to {:.3f} m ({:.1f} wavelengths)'.format(radii[0], radii[0]/wavelength, radii[-1], radii[-1]/wavelength))
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print('Theoretical boundary between reactive & radiating near-field (0.62*sqrt((D^3/wavelength): {:.3f} m'.format(0.62 * np.sqrt((D**3)/wavelength)))
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print('Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m'.format((2 * D**2)/wavelength))
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# Load text file with coordinates of pattern origin
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origin = np.loadtxt(os.path.splitext(outputfile)[0] + '_rxsorigin.txt')
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# Load output file and read some header information
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f = h5py.File(outputfile, 'r')
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iterations = f.attrs['Iterations']
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dt = f.attrs['dt']
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nrx = f.attrs['nrx']
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if antenna:
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nrx = nrx - 1 # Ignore first receiver point with full antenna model
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start = 2
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else:
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start = 1
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time = np.arange(0, dt * iterations, dt)
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time = time / 1E-9
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fs = 1 / dt # Sampling frequency
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# Initialise arrays to store fields
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coords = np.zeros((nrx, 3), dtype=np.float32)
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Ex = np.zeros((iterations, nrx), dtype=np.float32)
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Ey = np.zeros((iterations, nrx), dtype=np.float32)
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Ez = np.zeros((iterations, nrx), dtype=np.float32)
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Hx = np.zeros((iterations, nrx), dtype=np.float32)
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Hy = np.zeros((iterations, nrx), dtype=np.float32)
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Hz = np.zeros((iterations, nrx), dtype=np.float32)
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Er = np.zeros((iterations, nrx), dtype=np.float32)
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Etheta = np.zeros((iterations, nrx), dtype=np.float32)
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Ephi = np.zeros((iterations, nrx), dtype=np.float32)
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Hr = np.zeros((iterations, nrx), dtype=np.float32)
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Htheta = np.zeros((iterations, nrx), dtype=np.float32)
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Hphi = np.zeros((iterations, nrx), dtype=np.float32)
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Ethetasum = np.zeros(len(theta), dtype=np.float32)
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Hthetasum = np.zeros(len(theta), dtype=np.float32)
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patternsave = np.zeros((len(radii), len(theta)), dtype=np.float32)
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for rx in range(0, nrx):
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path = '/rxs/rx' + str(rx + start) + '/'
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position = f[path].attrs['Position'][:]
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coords[rx, :] = position - origin
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Ex[:, rx] = f[path + 'Ex'][:]
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Ey[:, rx] = f[path + 'Ey'][:]
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Ez[:, rx] = f[path + 'Ez'][:]
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Hx[:, rx] = f[path + 'Hx'][:]
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Hy[:, rx] = f[path + 'Hy'][:]
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Hz[:, rx] = f[path + 'Hz'][:]
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f.close()
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## Plot traces for sanity checking
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#fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(num=outputfile, nrows=3, ncols=2, sharex=False, sharey='col', subplot_kw=dict(xlabel='Time [ns]'), figsize=(20, 10), facecolor='w', edgecolor='w')
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#ax1.plot(time, Ex[:, traceno],'r', lw=2)
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#ax1.set_ylabel('$E_x$, field strength [V/m]')
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#ax3.plot(time, Ey[:, traceno],'r', lw=2)
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#ax3.set_ylabel('$E_y$, field strength [V/m]')
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#ax5.plot(time, Ez[:, traceno],'r', lw=2)
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#ax5.set_ylabel('$E_z$, field strength [V/m]')
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#ax2.plot(time, Hx[:, traceno],'b', lw=2)
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#ax2.set_ylabel('$H_x$, field strength [A/m]')
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#ax4.plot(time, Hy[:, traceno],'b', lw=2)
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#ax4.set_ylabel('$H_y$, field strength [A/m]')
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#ax6.plot(time, Hz[:, traceno],'b', lw=2)
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#ax6.set_ylabel('$H_z$, field strength [A/m]')
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## Turn on grid
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#[ax.grid() for ax in fig.axes]
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#plt.show()
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# Calculate fields for patterns
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rxstart = 0 # Index for rx points
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for radius in range(0, len(radii)):
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index = 0
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# Observation points
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for pt in range(rxstart, rxstart + len(theta)):
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# Cartesian to spherical coordinate transform coefficients from (Kraus,1991,Electromagnetics,p.34)
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r1 = coords[pt, 0] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2)
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r2 = coords[pt, 1] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2)
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r3 = coords[pt, 2] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2)
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theta1 = (coords[pt, 0] * coords[pt, 2]) / (np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2) * np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2))
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theta2 = (coords[pt, 1] * coords[pt, 2]) / (np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2) * np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2))
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theta3 = -(np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2) / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2))
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phi1 = -(coords[pt, 1] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2))
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phi2 = coords[pt, 0] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2)
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phi3 = 0
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# Fields in spherical coordinates
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Er[:, index] = Ex[:, pt] * r1 + Ey[:, pt] * r2 + Ez[:, pt] * r3
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Etheta[:, index] = Ex[:, pt] * theta1 + Ey[:, pt] * theta2 + Ez[:, pt] * theta3
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Ephi[:, index] = Ex[:, pt] * phi1 + Ey[:, pt] * phi2 + Ez[:, pt] * phi3
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Hr[:, index] = Hx[:, pt] * r1 + Hy[:, pt] * r2 + Hz[:, pt] * r3
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Htheta[:, index] = Hx[:, pt] * theta1 + Hy[:, pt] * theta2 + Hz[:, pt] * theta3
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Hphi[:, index] = Hx[:, pt] * phi1 + Hy[:, pt] * phi2 + Hz[:, pt] * phi3
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# Calculate metric for time-domain field pattern values
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if impscaling and coords[pt, 2] < 0:
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Ethetasum[index] = np.sum(Etheta[:, index]**2) / z1
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Hthetasum[index] = np.sum(Htheta[:, index]**2) / z1
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else:
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Ethetasum[index] = np.sum(Etheta[:, index]**2) / z0
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Hthetasum[index] = np.sum(Htheta[:, index]**2) / z0
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index += 1
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if type == 'H':
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# Flip H-plane patterns as rx points are written CCW but always plotted CW
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patternsave[radius, :] = Hthetasum[::-1]
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elif type == 'E':
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patternsave[radius, :] = Ethetasum
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rxstart += len(theta)
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# Save pattern to numpy file
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np.save(os.path.splitext(outputfile)[0], patternsave)
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print('Written Numpy file: {}.npy'.format(os.path.splitext(outputfile)[0]))
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# Copyright (C) 2016, Craig Warren
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#
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# This module is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
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# To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/.
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#
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# Please use the attribution at http://dx.doi.org/10.1016/j.sigpro.2016.04.010
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import argparse
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from gprMax.constants import c, z0
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='Plot field patterns from a simulation with receivers positioned in circles around an antenna. This module should be used after the field pattern data has been processed and stored using the initial_save.py module.', usage='cd gprMax; python -m user_libs.antenna_patterns.plot_fields numpyfile')
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parser.add_argument('numpyfile', help='name of numpy file including path')
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#parser.add_argument('hertzian', help='name of numpy file including path')
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args = parser.parse_args()
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patterns = np.load(args.numpyfile)
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#hertzian = np.load(args.hertzian)
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########################################
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# User configurable parameters
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# Pattern type (E or H)
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type = 'H'
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# Relative permittivity of half-space for homogeneous materials (set to None for inhomogeneous)
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epsr = 5
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# Observation radii and angles
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radii = np.linspace(0.1, 0.3, 20)
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theta = np.linspace(3, 357, 60)
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theta = np.deg2rad(np.append(theta, theta[0])) # Append start value to close circle
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# Centre frequency of modelled antenna
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f = 1.5e9 # GSSI 1.5GHz antenna model
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# Largest dimension of antenna transmitting element
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D = 0.060 # GSSI 1.5GHz antenna model
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# Minimum value for plotting energy and ring steps (dB)
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min = -72
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step = 12
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########################################
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# Critical angle and velocity
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if epsr:
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mr = 1
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z1 = np.sqrt(mr/epsr) * z0
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v1 = c / np.sqrt(epsr)
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thetac = np.round(np.rad2deg(np.arcsin(v1/c)))
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wavelength = v1/f
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# Print some useful information
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print('Centre frequency: {} GHz'.format(f/1e9))
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if epsr:
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print('Critical angle for Er {} is {} degrees'.format(epsr, thetac))
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print('Wavelength: {:.3f} m'.format(wavelength))
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print('Observation distance(s) from {:.3f} m ({:.1f} wavelengths) to {:.3f} m ({:.1f} wavelengths)'.format(radii[0], radii[0]/wavelength, radii[-1], radii[-1]/wavelength))
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print('Theoretical boundary between reactive & radiating near-field (0.62*sqrt((D^3/wavelength): {:.3f} m'.format(0.62 * np.sqrt((D**3)/wavelength)))
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print('Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m'.format((2 * D**2)/wavelength))
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# Setup figure
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fig = plt.figure(num=args.numpyfile, figsize=(8, 8), facecolor='w', edgecolor='w')
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ax = plt.subplot(111, polar=True)
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cmap = plt.cm.get_cmap('rainbow')
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ax.set_prop_cycle('color', [cmap(i) for i in np.linspace(0, 1, len(radii))])
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# Critical angle window and air/subsurface interface lines
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if epsr:
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ax.plot([0, np.deg2rad(180 - thetac)], [min, 0], color='0.7', lw=2)
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ax.plot([0, np.deg2rad(180 + thetac)], [min, 0], color='0.7', lw=2)
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ax.plot([np.deg2rad(270), np.deg2rad(90)], [0, 0], color='0.7', lw=2)
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ax.annotate('Air', xy=(np.deg2rad(270), 0), xytext=(8, 8), textcoords='offset points')
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ax.annotate('Ground', xy=(np.deg2rad(270), 0), xytext=(8, -15), textcoords='offset points')
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# Plot patterns
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for patt in range(0, len(radii)):
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pattplot = np.append(patterns[patt, :], patterns[patt, 0]) # Append start value to close circle
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pattplot = pattplot / np.max(np.max(patterns)) # Normalise, based on set of patterns
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ax.plot(theta, 10 * np.log10(pattplot), label='{:.2f}m'.format(radii[patt]), marker='.', ms=6, lw=1.5)
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# Add Hertzian dipole plot
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#hertzplot1 = np.append(hertzian[0, :], hertzian[0, 0]) # Append start value to close circle
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#hertzplot1 = hertzplot1 / np.max(np.max(hertzian))
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#ax.plot(theta, 10 * np.log10(hertzplot1), label='Inf. dipole, 0.1m', color='black', ls='-.', lw=3)
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#hertzplot2 = np.append(hertzian[-1, :], hertzian[-1, 0]) # Append start value to close circle
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#hertzplot2 = hertzplot2 / np.max(np.max(hertzian))
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#ax.plot(theta, 10 * np.log10(hertzplot2), label='Inf. dipole, 0.58m', color='black', ls='--', lw=3)
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# Theta axis options
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ax.set_theta_zero_location('N')
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ax.set_theta_direction('clockwise')
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ax.set_thetagrids(np.arange(0, 360, 30), frac=1.1)
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# Radial axis options
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ax.set_rmax(0)
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ax.set_rlabel_position(45)
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ax.set_yticks(np.arange(min, step, step))
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yticks = ax.get_yticks().tolist()
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yticks[-1]='0 dB'
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ax.set_yticklabels(yticks)
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# Grid and legend
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ax.grid(True)
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handles, existlabels = ax.get_legend_handles_labels()
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leg = ax.legend([handles[0], handles[-1]], [existlabels[0], existlabels[-1]], ncol=2, loc=(0.27,-0.12), frameon=False) # Plot just first and last legend entries
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#leg = ax.legend([handles[0], handles[-3], handles[-2], handles[-1]], [existlabels[0], existlabels[-3], existlabels[-2], existlabels[-1]], ncol=4, loc=(-0.13,-0.12), frameon=False)
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[legobj.set_linewidth(2) for legobj in leg.legendHandles]
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# Save a pdf of the plot
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savename = os.path.splitext(args.numpyfile)[0] + '.pdf'
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fig.savefig(savename, dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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#savename = os.path.splitext(args.numpyfile)[0] + '.png'
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#fig.savefig(savename, dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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plt.show()
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