publicImage/gprMax
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镜像自地址 https://gitee.com/sunhf/gprMax.git 已同步 2025-08-07 23:14:03 +08:00
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Added a pre-commit config file and reformatted all the files accordingly by using it.

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Sai-Suraj-27
2023-06-26 16:09:39 +05:30
父节点 c71e87e34f
当前提交 f9dd7f2420
共有 155 个文件被更改，包括 11383 次插入 和 8802 次删除
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106
toolboxes/AntennaPatterns/initial_save.py
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@@ -18,8 +18,11 @@ logger = logging.getLogger(__name__)
# Parse command line arguments
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')
parser.add_argument('outputfile', help='name of gprMax output file including path')
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",
)
parser.add_argument("outputfile", help="name of gprMax output file including path")
args = parser.parse_args()
outputfile = args.outputfile
@@ -27,7 +30,7 @@ outputfile = args.outputfile
# User configurable parameters
# Pattern type (E or H)
type = 'H'
type = "H"
# Antenna (true if using full antenna model; false for a theoretical Hertzian dipole
antenna = True
@@ -55,35 +58,47 @@ traceno = np.s_[:] # All traces
# Critical angle and velocity
if epsr:
mr = 1
z1 = np.sqrt(mr / epsr) * config.sim_config.em_consts['z0']
v1 = config.sim_config.em_consts['c'] / np.sqrt(epsr)
thetac = np.round(np.arcsin(v1 / config.sim_config.em_consts['c']) * (180 / np.pi))
z1 = np.sqrt(mr / epsr) * config.sim_config.em_consts["z0"]
v1 = config.sim_config.em_consts["c"] / np.sqrt(epsr)
thetac = np.round(np.arcsin(v1 / config.sim_config.em_consts["c"]) * (180 / np.pi))
wavelength = v1 / f
# Print some useful information
logger.info('Centre frequency: {} GHz'.format(f / 1e9))
logger.info("Centre frequency: {} GHz".format(f / 1e9))
if epsr:
logger.info('Critical angle for Er {} is {} degrees'.format(epsr, thetac))
logger.info('Wavelength: {:.3f} m'.format(wavelength))
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))
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)))
logger.info('Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m'.format((2 * D**2) / wavelength))
logger.info("Critical angle for Er {} is {} degrees".format(epsr, thetac))
logger.info("Wavelength: {:.3f} m".format(wavelength))
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
)
)
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)
)
)
logger.info(
"Theoretical boundary between radiating near-field & far-field (2*D^2/wavelength): {:.3f} m".format(
(2 * D**2) / wavelength
)
)
# Load text file with coordinates of pattern origin
origin = np.loadtxt(os.path.splitext(outputfile)[0] + '_rxsorigin.txt')
origin = np.loadtxt(os.path.splitext(outputfile)[0] + "_rxsorigin.txt")
# Load output file and read some header information
f = h5py.File(outputfile, 'r')
iterations = f.attrs['Iterations']
dt = f.attrs['dt']
nrx = f.attrs['nrx']
f = h5py.File(outputfile, "r")
iterations = f.attrs["Iterations"]
dt = f.attrs["dt"]
nrx = f.attrs["nrx"]
if antenna:
nrx = nrx - 1 # Ignore first receiver point with full antenna model
start = 2
else:
start = 1
time = np.arange(0, dt * iterations, dt)
time = time / 1E-9
time = time / 1e-9
fs = 1 / dt # Sampling frequency
# Initialise arrays to store fields
@@ -105,15 +120,15 @@ Hthetasum = np.zeros(len(theta), dtype=np.float32)
patternsave = np.zeros((len(radii), len(theta)), dtype=np.float32)
for rx in range(0, nrx):
path = '/rxs/rx' + str(rx + start) + '/'
position = f[path].attrs['Position'][:]
path = "/rxs/rx" + str(rx + start) + "/"
position = f[path].attrs["Position"][:]
coords[rx, :] = position - origin
Ex[:, rx] = f[path + 'Ex'][:]
Ey[:, rx] = f[path + 'Ey'][:]
Ez[:, rx] = f[path + 'Ez'][:]
Hx[:, rx] = f[path + 'Hx'][:]
Hy[:, rx] = f[path + 'Hy'][:]
Hz[:, rx] = f[path + 'Hz'][:]
Ex[:, rx] = f[path + "Ex"][:]
Ey[:, rx] = f[path + "Ey"][:]
Ez[:, rx] = f[path + "Ez"][:]
Hx[:, rx] = f[path + "Hx"][:]
Hy[:, rx] = f[path + "Hy"][:]
Hz[:, rx] = f[path + "Hz"][:]
f.close()
# Plot traces for sanity checking
@@ -141,14 +156,23 @@ for radius in range(0, len(radii)):
# Observation points
for pt in range(rxstart, rxstart + len(theta)):
# Cartesian to spherical coordinate transform coefficients from (Kraus,1991,Electromagnetics,p.34)
r1 = coords[pt, 0] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2)
r2 = coords[pt, 1] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2)
r3 = coords[pt, 2] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2 + coords[pt, 2]**2)
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))
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))
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))
phi1 = -(coords[pt, 1] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2))
phi2 = coords[pt, 0] / np.sqrt(coords[pt, 0]**2 + coords[pt, 1]**2)
r1 = coords[pt, 0] / np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2 + coords[pt, 2] ** 2)
r2 = coords[pt, 1] / np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2 + coords[pt, 2] ** 2)
r3 = coords[pt, 2] / np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2 + coords[pt, 2] ** 2)
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)
)
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)
)
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)
)
phi1 = -(coords[pt, 1] / np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2))
phi2 = coords[pt, 0] / np.sqrt(coords[pt, 0] ** 2 + coords[pt, 1] ** 2)
phi3 = 0
# Fields in spherical coordinates
@@ -161,22 +185,22 @@ for radius in range(0, len(radii)):
# Calculate metric for time-domain field pattern values
if impscaling and coords[pt, 2] < 0:
Ethetasum[index] = np.sum(Etheta[:, index]**2) / z1
Hthetasum[index] = np.sum(Htheta[:, index]**2) / z1
Ethetasum[index] = np.sum(Etheta[:, index] ** 2) / z1
Hthetasum[index] = np.sum(Htheta[:, index] ** 2) / z1
else:
Ethetasum[index] = np.sum(Etheta[:, index]**2) / config.sim_config.em_consts['z0']
Hthetasum[index] = np.sum(Htheta[:, index]**2) / config.sim_config.em_consts['z0']
Ethetasum[index] = np.sum(Etheta[:, index] ** 2) / config.sim_config.em_consts["z0"]
Hthetasum[index] = np.sum(Htheta[:, index] ** 2) / config.sim_config.em_consts["z0"]
index += 1
if type == 'H':
if type == "H":
# Flip H-plane patterns as rx points are written CCW but always plotted CW
patternsave[radius, :] = Hthetasum[::-1]
elif type == 'E':
elif type == "E":
patternsave[radius, :] = Ethetasum
rxstart += len(theta)
# Save pattern to numpy file
np.save(os.path.splitext(outputfile)[0], patternsave)
logger.info('Written Numpy file: {}.npy'.format(os.path.splitext(outputfile)[0]))
logger.info("Written Numpy file: {}.npy".format(os.path.splitext(outputfile)[0]))