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https://gitee.com/sunhf/gprMax.git
已同步 2025-08-08 07:24:19 +08:00
Added a pre-commit config file and reformatted all the files accordingly by using it.
这个提交包含在：
Sai-Suraj-27
@@ -18,8 +18,11 @@ logger = logging.getLogger(__name__)
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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.AntennaPatterns.initial_save outputfile')
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parser.add_argument('outputfile', help='name of gprMax output file including path')
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parser = argparse.ArgumentParser(
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description="Calculate and store (in a Numpy file) field patterns from a simulation with receivers positioned in circles around an antenna.",
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usage="cd gprMax; python -m user_libs.AntennaPatterns.initial_save outputfile",
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)
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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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@@ -27,7 +30,7 @@ outputfile = args.outputfile
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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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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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@@ -55,35 +58,47 @@ traceno = np.s_[:] # All traces
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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) * config.sim_config.em_consts['z0']
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v1 = config.sim_config.em_consts['c'] / np.sqrt(epsr)
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thetac = np.round(np.arcsin(v1 / config.sim_config.em_consts['c']) * (180 / np.pi))
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z1 = np.sqrt(mr / epsr) * config.sim_config.em_consts["z0"]
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v1 = config.sim_config.em_consts["c"] / np.sqrt(epsr)
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thetac = np.round(np.arcsin(v1 / config.sim_config.em_consts["c"]) * (180 / np.pi))
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wavelength = v1 / f
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# Print some useful information
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logger.info('Centre frequency: {} GHz'.format(f / 1e9))
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logger.info("Centre frequency: {} GHz".format(f / 1e9))
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if epsr:
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logger.info('Critical angle for Er {} is {} degrees'.format(epsr, thetac))
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logger.info('Wavelength: {:.3f} m'.format(wavelength))
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logger.info('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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logger.info('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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logger.info('Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m'.format((2 * D**2) / wavelength))
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logger.info("Critical angle for Er {} is {} degrees".format(epsr, thetac))
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logger.info("Wavelength: {:.3f} m".format(wavelength))
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logger.info(
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"Observation distance(s) from {:.3f} m ({:.1f} wavelengths) to {:.3f} m ({:.1f} wavelengths)".format(
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radii[0], radii[0] / wavelength, radii[-1], radii[-1] / wavelength
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)
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)
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logger.info(
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"Theoretical boundary between reactive & radiating near-field (0.62*sqrt((D^3/wavelength): {:.3f} m".format(
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0.62 * np.sqrt((D**3) / wavelength)
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)
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)
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logger.info(
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"Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m".format(
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(2 * D**2) / wavelength
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)
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)
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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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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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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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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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@@ -105,15 +120,15 @@ 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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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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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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@@ -141,14 +156,23 @@ for radius in range(0, len(radii)):
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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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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]) / (
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np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2)
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* np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2 + coords[pt, 2] ** 2)
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)
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theta2 = (coords[pt, 1] * coords[pt, 2]) / (
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np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2)
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* np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2 + coords[pt, 2] ** 2)
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)
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theta3 = -(
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np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2)
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/ np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2 + coords[pt, 2] ** 2)
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)
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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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@@ -161,22 +185,22 @@ for radius in range(0, len(radii)):
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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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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) / config.sim_config.em_consts['z0']
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Hthetasum[index] = np.sum(Htheta[:, index]**2) / config.sim_config.em_consts['z0']
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Ethetasum[index] = np.sum(Etheta[:, index] ** 2) / config.sim_config.em_consts["z0"]
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Hthetasum[index] = np.sum(Htheta[:, index] ** 2) / config.sim_config.em_consts["z0"]
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index += 1
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if type == 'H':
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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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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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logger.info('Written Numpy file: {}.npy'.format(os.path.splitext(outputfile)[0]))
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logger.info("Written Numpy file: {}.npy".format(os.path.splitext(outputfile)[0]))
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@@ -17,8 +17,11 @@ logger = logging.getLogger(__name__)
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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.AntennaPatterns.plot_fields numpyfile')
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parser.add_argument('numpyfile', help='name of numpy file including path')
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parser = argparse.ArgumentParser(
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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.",
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usage="cd gprMax; python -m user_libs.AntennaPatterns.plot_fields numpyfile",
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)
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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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@@ -28,7 +31,7 @@ patterns = np.load(args.numpyfile)
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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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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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@@ -52,33 +55,45 @@ step = 12
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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) * config.sim_config.em_consts['z0']
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v1 = config.sim_config.em_consts['c'] / np.sqrt(epsr)
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thetac = np.round(np.rad2deg(np.arcsin(v1 / config.sim_config.em_consts['c'])))
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z1 = np.sqrt(mr / epsr) * config.sim_config.em_consts["z0"]
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v1 = config.sim_config.em_consts["c"] / np.sqrt(epsr)
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thetac = np.round(np.rad2deg(np.arcsin(v1 / config.sim_config.em_consts["c"])))
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wavelength = v1 / f
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# Print some useful information
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logger.info('Centre frequency: {} GHz'.format(f / 1e9))
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logger.info("Centre frequency: {} GHz".format(f / 1e9))
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if epsr:
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logger.info('Critical angle for Er {} is {} degrees'.format(epsr, thetac))
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logger.info('Wavelength: {:.3f} m'.format(wavelength))
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logger.info('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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logger.info('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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logger.info('Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m'.format((2 * D**2) / wavelength))
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logger.info("Critical angle for Er {} is {} degrees".format(epsr, thetac))
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logger.info("Wavelength: {:.3f} m".format(wavelength))
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logger.info(
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"Observation distance(s) from {:.3f} m ({:.1f} wavelengths) to {:.3f} m ({:.1f} wavelengths)".format(
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radii[0], radii[0] / wavelength, radii[-1], radii[-1] / wavelength
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)
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)
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logger.info(
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"Theoretical boundary between reactive & radiating near-field (0.62*sqrt((D^3/wavelength): {:.3f} m".format(
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0.62 * np.sqrt((D**3) / wavelength)
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)
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)
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logger.info(
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"Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m".format(
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(2 * D**2) / wavelength
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)
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)
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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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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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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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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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@@ -86,12 +101,12 @@ for patt in range(0, len(radii)):
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pattplot = pattplot / np.max(np.max(patterns)) # Normalise, based on set of patterns
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# Calculate power (ignore warning from taking a log of any zero values)
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with np.errstate(divide='ignore'):
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with np.errstate(divide="ignore"):
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power = 10 * np.log10(pattplot)
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# Replace any NaNs or Infs from zero division
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power[np.invert(np.isfinite(power))] = 0
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ax.plot(theta, power, label='{:.2f}m'.format(radii[patt]), marker='.', ms=6, lw=1.5)
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ax.plot(theta, power, 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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@@ -102,8 +117,8 @@ for patt in range(0, len(radii)):
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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_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))
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# Radial axis options
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@@ -111,19 +126,21 @@ 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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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(
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[handles[0], handles[-1]], [existlabels[0], existlabels[-1]], ncol=2, loc=(0.27, -0.12), frameon=False
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) # 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] + ".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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在新工单中引用
屏蔽一个用户