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已同步 2025-08-07 23:14:03 +08:00
Build subgrids from Model and simplify FDTDGrid.build()
Subgrids are no longer contained within an FDTDGrid. The FDTDGrid.build() function now just builds the grid it is called on. The Model class now checks memory requirements etc.
这个提交包含在：
nmannall
@@ -23,7 +23,6 @@ import sys
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from collections import OrderedDict
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from typing import Any, Iterable, List, Union
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import humanize
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import numpy as np
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from terminaltables import SingleTable
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from tqdm import tqdm
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@@ -40,7 +39,6 @@ from gprMax.snapshots import Snapshot
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from gprMax.sources import HertzianDipole, MagneticDipole, Source, TransmissionLine, VoltageSource
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# from gprMax.subgrids.grid import SubGridBaseGrid
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from gprMax.utilities.host_info import mem_check_build_all, mem_check_run_all
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from gprMax.utilities.utilities import fft_power, get_terminal_width, round_value
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from gprMax.waveforms import Waveform
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@@ -138,105 +136,22 @@ class FDTDGrid:
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self.dl[2] = value
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def build(self) -> None:
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# Print info on any subgrids
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for subgrid in self.subgrids:
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subgrid.print_info()
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# Combine available grids
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grids = [self] + self.subgrids
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# Check for dispersive materials (and specific type)
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if config.get_model_config().materials["maxpoles"] != 0:
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# TODO: This sets materials["drudelorentz"] based only the
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# last grid/subgrid. Is that correct?
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for grid in grids:
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config.get_model_config().materials["drudelorentz"] = any(
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[m for m in grid.materials if "drude" in m.type or "lorentz" in m.type]
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)
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# Set data type if any dispersive materials (must be done before memory checks)
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config.get_model_config().set_dispersive_material_types()
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# Check memory requirements to build model/scene (different to memory
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# requirements to run model when FractalVolumes/FractalSurfaces are
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# used as these can require significant additional memory)
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total_mem_build, mem_strs_build = mem_check_build_all(grids)
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# Check memory requirements to run model
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total_mem_run, mem_strs_run = mem_check_run_all(grids)
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if total_mem_build > total_mem_run:
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logger.info(
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f'\nMemory required (estimated): {" + ".join(mem_strs_build)} + '
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f"~{humanize.naturalsize(config.get_model_config().mem_overhead)} "
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f"overhead = {humanize.naturalsize(total_mem_build)}"
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)
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else:
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logger.info(
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f'\nMemory required (estimated): {" + ".join(mem_strs_run)} + '
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f"~{humanize.naturalsize(config.get_model_config().mem_overhead)} "
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f"overhead = {humanize.naturalsize(total_mem_run)}"
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)
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# Build grids
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for grid in grids:
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# Set default CFS parameter for PMLs if not user provided
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if not grid.pmls["cfs"]:
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grid.pmls["cfs"] = [CFS()]
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logger.info(print_pml_info(grid))
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if not all(value == 0 for value in grid.pmls["thickness"].values()):
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grid._build_pmls()
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if grid.averagevolumeobjects:
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grid._build_components()
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grid._tm_grid_update()
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grid._update_voltage_source_materials()
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grid.initialise_field_arrays()
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grid.initialise_std_update_coeff_arrays()
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if config.get_model_config().materials["maxpoles"] > 0:
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grid.initialise_dispersive_arrays()
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grid.initialise_dispersive_update_coeff_array()
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grid._build_materials()
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# Check to see if numerical dispersion might be a problem
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results = dispersion_analysis(grid)
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if results["error"]:
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logger.warning(
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f"\nNumerical dispersion analysis [{grid.name}] "
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f"not carried out as {results['error']}"
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)
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elif results["N"] < config.get_model_config().numdispersion["mingridsampling"]:
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logger.exception(
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f"\nNon-physical wave propagation in [{grid.name}] "
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f"detected. Material '{results['material'].ID}' "
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f"has wavelength sampled by {results['N']} cells, "
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f"less than required minimum for physical wave "
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f"propagation. Maximum significant frequency "
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f"estimated as {results['maxfreq']:g}Hz"
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)
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raise ValueError
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elif (
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results["deltavp"]
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and np.abs(results["deltavp"])
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> config.get_model_config().numdispersion["maxnumericaldisp"]
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):
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logger.warning(
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f"\n[{grid.name}] has potentially significant "
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f"numerical dispersion. Estimated largest physical "
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f"phase-velocity error is {results['deltavp']:.2f}% "
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f"in material '{results['material'].ID}' whose "
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f"wavelength sampled by {results['N']} cells. "
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f"Maximum significant frequency estimated as "
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f"{results['maxfreq']:g}Hz"
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)
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elif results["deltavp"]:
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logger.info(
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f"\nNumerical dispersion analysis [{grid.name}]: "
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f"estimated largest physical phase-velocity error is "
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f"{results['deltavp']:.2f}% in material '{results['material'].ID}' "
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f"whose wavelength sampled by {results['N']} cells. "
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f"Maximum significant frequency estimated as "
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f"{results['maxfreq']:g}Hz"
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)
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# Set default CFS parameter for PMLs if not user provided
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if not self.pmls["cfs"]:
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self.pmls["cfs"] = [CFS()]
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logger.info(print_pml_info(self))
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if not all(value == 0 for value in self.pmls["thickness"].values()):
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self._build_pmls()
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if self.averagevolumeobjects:
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self._build_components()
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self._tm_grid_update()
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self._create_voltage_source_materials()
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self.initialise_field_arrays()
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self.initialise_std_update_coeff_arrays()
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if config.get_model_config().materials["maxpoles"] > 0:
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self.initialise_dispersive_arrays()
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self.initialise_dispersive_update_coeff_array()
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self._build_materials()
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def _build_pmls(self) -> None:
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pbar = tqdm(
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@@ -277,7 +192,7 @@ class FDTDGrid:
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elif config.get_model_config().mode == "2D TMz":
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self.tmz()
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def _update_voltage_source_materials(self):
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def _create_voltage_source_materials(self):
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# Process any voltage sources (that have resistance) to create a new
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# material at the source location
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for voltagesource in self.voltagesources:
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@@ -651,138 +566,183 @@ class FDTDGrid:
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return Iz
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def dispersion_analysis(G):
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"""Analysis of numerical dispersion (Taflove et al, 2005, p112) -
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worse case of maximum frequency and minimum wavelength
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Args:
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G: FDTDGrid class describing a grid in a model.
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Returns:
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results: dict of results from dispersion analysis.
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"""
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# deltavp: physical phase velocity error (percentage)
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# N: grid sampling density
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# material: material with maximum permittivity
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# maxfreq: maximum significant frequency
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# error: error message
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results = {"deltavp": None, "N": None, "material": None, "maxfreq": [], "error": ""}
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# Find maximum significant frequency
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if G.waveforms:
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for waveform in G.waveforms:
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if waveform.type in ["sine", "contsine"]:
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results["maxfreq"].append(4 * waveform.freq)
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elif waveform.type == "impulse":
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results["error"] = "impulse waveform used."
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elif waveform.type == "user":
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results["error"] = "user waveform detected."
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else:
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# Time to analyse waveform - 4*pulse_width as using entire
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# time window can result in demanding FFT
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waveform.calculate_coefficients()
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iterations = round_value(4 * waveform.chi / G.dt)
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iterations = min(iterations, G.iterations)
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waveformvalues = np.zeros(G.iterations)
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for iteration in range(G.iterations):
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waveformvalues[iteration] = waveform.calculate_value(iteration * G.dt, G.dt)
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# Ensure source waveform is not being overly truncated before attempting any FFT
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if np.abs(waveformvalues[-1]) < np.abs(np.amax(waveformvalues)) / 100:
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# FFT
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freqs, power = fft_power(waveformvalues, G.dt)
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# Get frequency for max power
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freqmaxpower = np.where(np.isclose(power, 0))[0][0]
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# Set maximum frequency to a threshold drop from maximum power, ignoring DC value
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try:
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freqthres = (
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np.where(
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power[freqmaxpower:]
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< -config.get_model_config().numdispersion["highestfreqthres"]
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)[0][0]
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+ freqmaxpower
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)
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results["maxfreq"].append(freqs[freqthres])
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except ValueError:
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results["error"] = (
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"unable to calculate maximum power "
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+ "from waveform, most likely due to "
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+ "undersampling."
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)
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# Ignore case where someone is using a waveform with zero amplitude, i.e. on a receiver
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elif waveform.amp == 0:
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pass
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# If waveform is truncated don't do any further analysis
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else:
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results["error"] = (
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"waveform does not fit within specified "
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+ "time window and is therefore being truncated."
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)
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else:
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results["error"] = "no waveform detected."
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if results["maxfreq"]:
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results["maxfreq"] = max(results["maxfreq"])
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# Find minimum wavelength (material with maximum permittivity)
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maxer = 0
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matmaxer = ""
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for x in G.materials:
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if x.se != float("inf"):
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er = x.er
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# If there are dispersive materials calculate the complex
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# relative permittivity at maximum frequency and take the real part
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if x.__class__.__name__ == "DispersiveMaterial":
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er = x.calculate_er(results["maxfreq"])
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er = er.real
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if er > maxer:
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maxer = er
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matmaxer = x.ID
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results["material"] = next(x for x in G.materials if x.ID == matmaxer)
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# Minimum velocity
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minvelocity = config.c / np.sqrt(maxer)
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# Minimum wavelength
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minwavelength = minvelocity / results["maxfreq"]
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# Maximum spatial step
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if "3D" in config.get_model_config().mode:
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delta = max(G.dx, G.dy, G.dz)
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elif "2D" in config.get_model_config().mode:
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if G.nx == 1:
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delta = max(G.dy, G.dz)
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elif G.ny == 1:
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delta = max(G.dx, G.dz)
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elif G.nz == 1:
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delta = max(G.dx, G.dy)
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# Courant stability factor
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S = (config.c * G.dt) / delta
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# Grid sampling density
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results["N"] = minwavelength / delta
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# Check grid sampling will result in physical wave propagation
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if (
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int(np.floor(results["N"]))
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>= config.get_model_config().numdispersion["mingridsampling"]
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def dispersion_analysis(self, iterations: int):
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# Check to see if numerical dispersion might be a problem
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results = self._dispersion_analysis(iterations)
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if results["error"]:
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logger.warning(
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f"\nNumerical dispersion analysis [{self.name}] "
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f"not carried out as {results['error']}"
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)
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elif results["N"] < config.get_model_config().numdispersion["mingridsampling"]:
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logger.exception(
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f"\nNon-physical wave propagation in [{self.name}] "
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f"detected. Material '{results['material'].ID}' "
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f"has wavelength sampled by {results['N']} cells, "
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f"less than required minimum for physical wave "
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f"propagation. Maximum significant frequency "
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f"estimated as {results['maxfreq']:g}Hz"
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)
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raise ValueError
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elif (
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results["deltavp"]
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and np.abs(results["deltavp"])
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> config.get_model_config().numdispersion["maxnumericaldisp"]
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):
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# Numerical phase velocity
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vp = np.pi / (results["N"] * np.arcsin((1 / S) * np.sin((np.pi * S) / results["N"])))
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logger.warning(
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f"\n[{self.name}] has potentially significant "
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f"numerical dispersion. Estimated largest physical "
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f"phase-velocity error is {results['deltavp']:.2f}% "
|
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f"in material '{results['material'].ID}' whose "
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f"wavelength sampled by {results['N']} cells. "
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f"Maximum significant frequency estimated as "
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f"{results['maxfreq']:g}Hz"
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)
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elif results["deltavp"]:
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logger.info(
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f"\nNumerical dispersion analysis [{self.name}]: "
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f"estimated largest physical phase-velocity error is "
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f"{results['deltavp']:.2f}% in material '{results['material'].ID}' "
|
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f"whose wavelength sampled by {results['N']} cells. "
|
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f"Maximum significant frequency estimated as "
|
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f"{results['maxfreq']:g}Hz"
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)
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# Physical phase velocity error (percentage)
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results["deltavp"] = (((vp * config.c) - config.c) / config.c) * 100
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def _dispersion_analysis(self, iterations: int):
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"""Analysis of numerical dispersion (Taflove et al, 2005, p112) -
|
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worse case of maximum frequency and minimum wavelength
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# Store rounded down value of grid sampling density
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results["N"] = int(np.floor(results["N"]))
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Args:
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G: FDTDGrid class describing a grid in a model.
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return results
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Returns:
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results: dict of results from dispersion analysis.
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"""
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# deltavp: physical phase velocity error (percentage)
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# N: grid sampling density
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# material: material with maximum permittivity
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# maxfreq: maximum significant frequency
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# error: error message
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results = {"deltavp": None, "N": None, "material": None, "maxfreq": [], "error": ""}
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# Find maximum significant frequency
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if self.waveforms:
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for waveform in self.waveforms:
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if waveform.type in ["sine", "contsine"]:
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results["maxfreq"].append(4 * waveform.freq)
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|
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elif waveform.type == "impulse":
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results["error"] = "impulse waveform used."
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|
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elif waveform.type == "user":
|
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results["error"] = "user waveform detected."
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|
||||
else:
|
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# Time to analyse waveform - 4*pulse_width as using entire
|
||||
# time window can result in demanding FFT
|
||||
waveform.calculate_coefficients()
|
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iterations = round_value(4 * waveform.chi / self.dt)
|
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iterations = min(iterations, iterations)
|
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waveformvalues = np.zeros(iterations)
|
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for iteration in range(iterations):
|
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waveformvalues[iteration] = waveform.calculate_value(
|
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iteration * self.dt, self.dt
|
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)
|
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|
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# Ensure source waveform is not being overly truncated before attempting any FFT
|
||||
if np.abs(waveformvalues[-1]) < np.abs(np.amax(waveformvalues)) / 100:
|
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# FFT
|
||||
freqs, power = fft_power(waveformvalues, self.dt)
|
||||
# Get frequency for max power
|
||||
freqmaxpower = np.where(np.isclose(power, 0))[0][0]
|
||||
|
||||
# Set maximum frequency to a threshold drop from maximum power, ignoring DC value
|
||||
try:
|
||||
freqthres = (
|
||||
np.where(
|
||||
power[freqmaxpower:]
|
||||
< -config.get_model_config().numdispersion["highestfreqthres"]
|
||||
)[0][0]
|
||||
+ freqmaxpower
|
||||
)
|
||||
results["maxfreq"].append(freqs[freqthres])
|
||||
except ValueError:
|
||||
results["error"] = (
|
||||
"unable to calculate maximum power "
|
||||
+ "from waveform, most likely due to "
|
||||
+ "undersampling."
|
||||
)
|
||||
|
||||
# Ignore case where someone is using a waveform with zero amplitude, i.e. on a receiver
|
||||
elif waveform.amp == 0:
|
||||
pass
|
||||
|
||||
# If waveform is truncated don't do any further analysis
|
||||
else:
|
||||
results["error"] = (
|
||||
"waveform does not fit within specified "
|
||||
+ "time window and is therefore being truncated."
|
||||
)
|
||||
else:
|
||||
results["error"] = "no waveform detected."
|
||||
|
||||
if results["maxfreq"]:
|
||||
results["maxfreq"] = max(results["maxfreq"])
|
||||
|
||||
# Find minimum wavelength (material with maximum permittivity)
|
||||
maxer = 0
|
||||
matmaxer = ""
|
||||
for x in self.materials:
|
||||
if x.se != float("inf"):
|
||||
er = x.er
|
||||
# If there are dispersive materials calculate the complex
|
||||
# relative permittivity at maximum frequency and take the real part
|
||||
if x.__class__.__name__ == "DispersiveMaterial":
|
||||
er = x.calculate_er(results["maxfreq"])
|
||||
er = er.real
|
||||
if er > maxer:
|
||||
maxer = er
|
||||
matmaxer = x.ID
|
||||
results["material"] = next(x for x in self.materials if x.ID == matmaxer)
|
||||
|
||||
# Minimum velocity
|
||||
minvelocity = config.c / np.sqrt(maxer)
|
||||
|
||||
# Minimum wavelength
|
||||
minwavelength = minvelocity / results["maxfreq"]
|
||||
|
||||
# Maximum spatial step
|
||||
if "3D" in config.get_model_config().mode:
|
||||
delta = max(self.dx, self.dy, self.dz)
|
||||
elif "2D" in config.get_model_config().mode:
|
||||
if self.nx == 1:
|
||||
delta = max(self.dy, self.dz)
|
||||
elif self.ny == 1:
|
||||
delta = max(self.dx, self.dz)
|
||||
elif self.nz == 1:
|
||||
delta = max(self.dx, self.dy)
|
||||
|
||||
# Courant stability factor
|
||||
S = (config.c * self.dt) / delta
|
||||
|
||||
# Grid sampling density
|
||||
results["N"] = minwavelength / delta
|
||||
|
||||
# Check grid sampling will result in physical wave propagation
|
||||
if (
|
||||
int(np.floor(results["N"]))
|
||||
>= config.get_model_config().numdispersion["mingridsampling"]
|
||||
):
|
||||
# Numerical phase velocity
|
||||
vp = np.pi / (
|
||||
results["N"] * np.arcsin((1 / S) * np.sin((np.pi * S) / results["N"]))
|
||||
)
|
||||
|
||||
# Physical phase velocity error (percentage)
|
||||
results["deltavp"] = (((vp * config.c) - config.c) / config.c) * 100
|
||||
|
||||
# Store rounded down value of grid sampling density
|
||||
results["N"] = int(np.floor(results["N"]))
|
||||
|
||||
return results
|
||||
|
||||
@@ -19,7 +19,7 @@
|
||||
import datetime
|
||||
import logging
|
||||
import sys
|
||||
from typing import List, Union
|
||||
from typing import List
|
||||
|
||||
import humanize
|
||||
import numpy as np
|
||||
@@ -28,7 +28,6 @@ from colorama import Fore, Style, init
|
||||
|
||||
from gprMax.grid.cuda_grid import CUDAGrid
|
||||
from gprMax.grid.opencl_grid import OpenCLGrid
|
||||
from gprMax.materials import ListMaterial, Material, PeplinskiSoil, RangeMaterial
|
||||
from gprMax.subgrids.grid import SubGridBaseGrid
|
||||
|
||||
init()
|
||||
@@ -41,7 +40,7 @@ from .fields_outputs import write_hdf5_outputfile
|
||||
from .geometry_outputs import GeometryObjects, GeometryView, save_geometry_views
|
||||
from .grid.fdtd_grid import FDTDGrid
|
||||
from .snapshots import save_snapshots
|
||||
from .utilities.host_info import set_omp_threads
|
||||
from .utilities.host_info import mem_check_build_all, mem_check_run_all, set_omp_threads
|
||||
from .utilities.utilities import get_terminal_width
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -206,11 +205,60 @@ class Model:
|
||||
|
||||
def build_geometry(self):
|
||||
logger.info(config.get_model_config().inputfilestr)
|
||||
# TODO: Make this correctly sets local nx, ny and nz when using MPI (likely use a function inside FDTDGrid/MPIGrid)
|
||||
|
||||
# Print info on any subgrids
|
||||
for subgrid in self.subgrids:
|
||||
subgrid.print_info()
|
||||
|
||||
# Combine available grids
|
||||
grids = [self.G] + self.subgrids
|
||||
|
||||
self._check_for_dispersive_materials(grids)
|
||||
self._check_memory_requirements(grids)
|
||||
|
||||
# TODO: Make this correctly set local nx, ny and nz when using MPI (likely use a function inside FDTDGrid/MPIGrid)
|
||||
self.G.nx = self.gnx
|
||||
self.G.ny = self.gny
|
||||
self.G.nz = self.gnz
|
||||
self.G.build()
|
||||
|
||||
for grid in grids:
|
||||
grid.build()
|
||||
grid.dispersion_analysis(self.iterations)
|
||||
|
||||
def _check_for_dispersive_materials(self, grids: List[FDTDGrid]):
|
||||
# Check for dispersive materials (and specific type)
|
||||
if config.get_model_config().materials["maxpoles"] != 0:
|
||||
# TODO: This sets materials["drudelorentz"] based only the
|
||||
# last grid/subgrid. Is that correct?
|
||||
for grid in grids:
|
||||
config.get_model_config().materials["drudelorentz"] = any(
|
||||
[m for m in grid.materials if "drude" in m.type or "lorentz" in m.type]
|
||||
)
|
||||
|
||||
# Set data type if any dispersive materials (must be done before memory checks)
|
||||
config.get_model_config().set_dispersive_material_types()
|
||||
|
||||
def _check_memory_requirements(self, grids: List[FDTDGrid]):
|
||||
# Check memory requirements to build model/scene (different to memory
|
||||
# requirements to run model when FractalVolumes/FractalSurfaces are
|
||||
# used as these can require significant additional memory)
|
||||
total_mem_build, mem_strs_build = mem_check_build_all(grids)
|
||||
|
||||
# Check memory requirements to run model
|
||||
total_mem_run, mem_strs_run = mem_check_run_all(grids)
|
||||
|
||||
if total_mem_build > total_mem_run:
|
||||
logger.info(
|
||||
f'\nMemory required (estimated): {" + ".join(mem_strs_build)} + '
|
||||
f"~{humanize.naturalsize(config.get_model_config().mem_overhead)} "
|
||||
f"overhead = {humanize.naturalsize(total_mem_build)}"
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
f'\nMemory required (estimated): {" + ".join(mem_strs_run)} + '
|
||||
f"~{humanize.naturalsize(config.get_model_config().mem_overhead)} "
|
||||
f"overhead = {humanize.naturalsize(total_mem_run)}"
|
||||
)
|
||||
|
||||
def reuse_geometry(self):
|
||||
s = (
|
||||
|
||||
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