Adding patterns

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craig-warren
2023-03-11 18:23:43 -07:00
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当前提交 6985350222
共有 7 个文件被更改,包括 157 次插入21 次删除

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.. _accelerators:
*********************************
Accelerators - OpenMP/CUDA/OpenCL
*********************************
******************
OpenMP/CUDA/OpenCL
******************
The most computationally intensive parts of gprMax, which are the FDTD solver loops, have been parallelised using different CPU and GPU accelerators to offer performance and flexibility.
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.. note::
You can use the ``get_host_spec.py`` module (in ``toolboxes/Utilities``) to help you understand what hardware (CPU/GPU) you have and how gprMax can use it with the aforementioned accelerators.
OpenMP
======
@@ -58,13 +58,13 @@ Software required
The following steps provide guidance on how to install the extra components to allow gprMax to run on your NVIDIA GPU:
1. Install the `NVIDIA CUDA Toolkit <https://developer.nvidia.com/cuda-toolkit>`_. You can follow the Installation Guides in the `NVIDIA CUDA Toolkit Documentation <http://docs.nvidia.com/cuda/index.html#installation-guides>`_ You must ensure the version of CUDA you install is compatible with the compiler you are using. This information can usually be found in a table in the CUDA Installation Guide under System Requirements.
2. You may need to add the location of the CUDA compiler (:code:`nvcc`) to your user path environment variable, e.g. for Windows :code:`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin` or Linux/macOS :code:`/Developer/NVIDIA/CUDA-10.0/bin`.
3. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`conda activate gprMax`. Run :code:`pip install pycuda`
2. You may need to add the location of the CUDA compiler (``nvcc``) to your user path environment variable, e.g. for Windows ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.X\bin`` or Linux/macOS ``/Developer/NVIDIA/CUDA-X.X/bin``.
3. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment ``conda activate gprMax``. Run ``pip install pycuda``
Example
-------
Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`conda activate gprMax`
Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment ``conda activate gprMax``
Run one of the test models:
@@ -74,7 +74,7 @@ Run one of the test models:
.. note::
If you want to select a specific GPU card on your system, you can specify an integer after the :code:`-gpu` flag. The integer should be the NVIDIA CUDA device ID for a specific GPU card. If it is not specified it defaults to device ID 0.
If you want to select a specific GPU card on your system, you can specify an integer after the ``-gpu`` flag. The integer should be the NVIDIA CUDA device ID for a specific GPU card. If it is not specified it defaults to device ID 0.
OpenCL
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.. note::
The argument given with `-mpi` is number of MPI tasks, i.e. master + workers, for MPI task farm. So in this case, 1 master (CPU) and 4 workers (GPU cards). The integers given with the `-gpu` argument are the NVIDIA CUDA device IDs for the specific GPU cards to be used.
The argument given with ``-mpi`` is number of MPI tasks, i.e. master + workers, for MPI task farm. So in this case, 1 master (CPU) and 4 workers (GPU cards). The integers given with the ``-gpu`` argument are the NVIDIA CUDA device IDs for the specific GPU cards to be used.

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.. _hpc:
**************************
High-performance computing
**************************
***
HPC
***
High-performance computing (HPC) environments usually require jobs to be submitted to a queue using a job script. The following are examples of job scripts for a HPC environment that uses `Open Grid Scheduler/Grid Engine <http://gridscheduler.sourceforge.net/index.html>`_, and are intended as general guidance to help you get started. Using gprMax in an HPC environment is heavily dependent on the configuration of your specific HPC/cluster, e.g. the names of parallel environments (``-pe``) and compiler modules will depend on how they were defined by your system administrator.

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from pathlib import Path
import numpy as np
import gprMax
from toolboxes.GPRAntennaModels.GSSI import antenna_like_GSSI_1500
# File path for output
fn = Path(__file__)
# Discretisation
dl = 0.001
scene = gprMax.Scene()
title = gprMax.Title(name=fn.with_suffix('').name)
dxdydz = gprMax.Discretisation(p1=(dl, dl, dl))
pml = gprMax.PMLProps(thickness=14)
scene.add(title)
scene.add(dxdydz)
scene.add(pml)
timewindows = np.array([4.5e-9]) # For 0.3m max
radii = np.linspace(0.1, 0.3, 20)
theta = np.linspace(3, 357, 60)
selector = 0
fs = np.array([0.040, 0.040, 0.040])
domain_size = np.array([2 * fs[0] + 0.170,
2 * fs[1] + 2 * radii[-1],
2 * fs[2] + 2 * radii[-1]])
domain = gprMax.Domain(p1=(domain_size[0], domain_size[1], domain_size[2]))
time_window = gprMax.TimeWindow(time=timewindows[selector])
scene.add(domain)
scene.add(time_window)
antennaposition = np.array([domain_size[0] / 2,
fs[1] + radii[-1],
fs[2] + radii[-1]])
gssi_objects = antenna_like_GSSI_1500(antennaposition[0],
antennaposition[1],
antennaposition[2])
for obj in gssi_objects:
scene.add(obj)
## Can introduce soil model
# soil = gprMax.SoilPeplinski(sand_fraction=0.5, clay_fraction=0.5,
# bulk_density=2.0, sand_density=2.66,
# water_fraction_lower=0.001,
# water_fraction_upper=0.25,
# id='mySoil')
# scene.add(soil)
# fbox = gprMax.FractalBox(p1=(0, 0, 0), p2=(domain_size[0], domain_size[1], fs[2] + radii[-1]),
# frac_dim=1.5, weighting=[1, 1, 1], n_materials=50,
# mixing_model_id=soil.id, id='mySoilBox')
# scene.add(fbox)
mat = gprMax.Material(er=5, se=0, mr=1, sm=0, id='er5')
scene.add(mat)
box = gprMax.Box(p1=(0, 0, 0), p2=(domain_size[0], domain_size[1], fs[2] + radii[-1]),
material_id='er5')
scene.add(box)
## Save the position of the antenna to file for use when processing results
np.savetxt(fn.with_suffix('').name + '_rxsorigin.txt', antennaposition, fmt="%f")
## Generate receiver points for pattern
for radius in range(len(radii)):
## E-plane circle (yz plane, x=0, phi=pi/2,3pi/2)
x = radii[radius] * np.sin(theta * np.pi /180) * np.cos(90 * np.pi / 180)
y = radii[radius] * np.sin(theta * np.pi /180) * np.sin(90 * np.pi / 180)
z = radii[radius] * np.cos(theta * np.pi /180)
for rxpt in range(len(theta)):
rx = gprMax.Rx(p1=(x[rxpt] + antennaposition[0],
y[rxpt] + antennaposition[1],
z[rxpt] + antennaposition[2]))
scene.add(rx)
gv1 = gprMax.GeometryView(p1=(0, 0, 0), p2=(domain_size[0], domain_size[1], domain_size[2]),
dl=(dl, dl, dl), filename='antenna_like_GSSI_1500_patterns',
output_type='n')
scene.add(gv1)
gprMax.run(scenes=[scene], geometry_only=True, outputfile=fn, gpu=None)

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#title: GSSI 1.5GHz antenna field patterns
#dx_dy_dz: 0.001 0.001 0.001
#pml_cells: 14
#python:
import os
import numpy as np
from gprMax.input_cmd_funcs import *
from user_libs.antennas.GSSI import antenna_like_GSSI_1500
filename = os.path.splitext(os.path.split(inputfile)[1])[0]
timewindows = np.array([4.5e-9]) # For 0.3m max
radii = np.linspace(0.1, 0.3, 20)
theta = np.linspace(3, 357, 60)
materials = ['5 0 1 0 er5'] # Can add more to list and use selector integer to choose
selector = 0
fs = np.array([0.040, 0.040, 0.040])
domain = np.array([2 * fs[0] + 2 * radii[-1], 2 * fs[1] + 0.107, 2 * fs[2] + 2 * radii[-1]])
antennaposition = np.array([fs[0] + radii[-1], domain[1] / 2, fs[2] + radii[-1]])
antenna_like_GSSI_1500(antennaposition[0], antennaposition[1], antennaposition[2])
print('#domain: {:.3f} {:.3f} {:.3f}'.format(domain[0], domain[1], domain[2]))
print('#time_window: {:.3e}'.format(timewindows[selector]))
## Can introduce soil model
#print('#soil_peplinski: 0.5 0.5 2.0 2.66 0.001 0.25 mySoil')
#print('#fractal_box: 0 0 0 {} {} {} 1.5 1 1 1 50 mySoil mySoilBox 1'.format(domain[0], domain[1], fs[2] + radii[-1]))
print('#material: {}'.format(materials[selector]))
print('#box: 0 0 0 {} {} {} {} n'.format(domain[0], domain[1], fs[2] + radii[-1], materials[selector].split()[-1]))
## Save the position of the antenna to file for use when processing results
np.savetxt(os.path.join(os.path.dirname(inputfile), filename + '_rxsorigin.txt'), antennaposition, fmt="%f")
## Generate receiver points for pattern
for radius in range(len(radii)):
## H-plane circle (xz plane, y=0, phi=0,pi)
x = radii[radius] * np.sin(theta * np.pi /180) * np.cos(180 * np.pi / 180)
y = radii[radius] * np.sin(theta * np.pi /180) * np.sin(180 * np.pi / 180)
z = radii[radius] * np.cos(theta * np.pi /180)
for rxpt in range(len(theta)):
print('#rx: {:.3f} {:.3f} {:.3f}'.format(x[rxpt] + antennaposition[0], y[rxpt] + antennaposition[1], z[rxpt] + antennaposition[2]))
geometry_view(0, 0, 0, domain[0], domain[1], domain[2], 0.001, 0.001, 0.001, filename, 'n')
#end_python:

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water fraction of the soil.
water_fraction_upper: float required for upper boundary of volumetric
water fraction of the soil.
id: string used as identifier for soil.
"""
def __init__(self, **kwargs):

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@@ -26,26 +26,26 @@ The package contains scripts to help calculate, process, and visualise field pat
Package contents
================
* `initial_save.py` is a module that calculates and stores (in a Numpy file) the field patterns from the output file of a simulation.
* `plot_fields.py` is a module that plots the field patterns. It should be used after the field pattern data has been processed and stored using the `initial_save.py` module.
* ``initial_save.py`` is a module that calculates and stores (in a Numpy file) the field patterns from the output file of a simulation.
* ``plot_fields.py`` is a module that plots the field patterns. It should be used after the field pattern data has been processed and stored using the ``initial_save.py`` module.
The package has been designed to work with input files found in the `examples` directory:
The package has been designed to work with input files found in the ``examples`` directory:
* `antenna_like_GSSI_1500_patterns_E.in` is an input file that includes an antenna model similar to a GSSI 1.5 GHz antenna and receivers to calculate a field pattern in the principal E-plane of the antenna
* `antenna_like_GSSI_1500_patterns_H.in` is an input file that includes an antenna model similar to a GSSI 1.5 GHz antenna and receivers to calculate a field pattern in the principal H-plane of the antenna
* ``antenna_like_GSSI_1500_patterns_E.in`` is an input file that includes an antenna model similar to a GSSI 1.5 GHz antenna and receivers to calculate a field pattern in the principal E-plane of the antenna
* ``antenna_like_GSSI_1500_patterns_H.in`` is an input file that includes an antenna model similar to a GSSI 1.5 GHz antenna and receivers to calculate a field pattern in the principal H-plane of the antenna
How to use the package
======================
* Firstly you should familiarise yourself with the example model input file. Edit the input file as desired and run one of the simulations for either E-plane or H-plane patterns.
* Whilst the simulation is running edit the 'user configurable parameters' sections of the `initial_save.py` and `plot_fields.py` modules to match the setup of the simulation.
* Once the simulation has completed, run the `initial_save.py` module on the output file, e.g. for the E-plane `python -m toolboxes.AntennaPatterns.initial_save examples/antenna_like_GSSI_1500_patterns_E_Er5.h5`. This will produce a Numpy file containing the field pattern data.
* Plot the field pattern data by running the `plot_fields.py` module on the Numpy file, e.g. for the E-plane `python -m toolboxes.AntennaPatterns.plot_fields examples/antenna_like_GSSI_1500_patterns_E_Er5.npy`
* Whilst the simulation is running edit the 'user configurable parameters' sections of the ``initial_save.py`` and ``plot_fields.py`` modules to match the setup of the simulation.
* Once the simulation has completed, run the ``initial_save.py`` module on the output file, e.g. for the E-plane ``python -m toolboxes.AntennaPatterns.initial_save examples/antenna_like_GSSI_1500_patterns_E_Er5.h5``. This will produce a Numpy file containing the field pattern data.
* Plot the field pattern data by running the ``plot_fields.py`` module on the Numpy file, e.g. for the E-plane ``python -m toolboxes.AntennaPatterns.plot_fields examples/antenna_like_GSSI_1500_patterns_E_Er5.npy``
.. tip::
If you want to create different plots you just need to edit and re-run the `plot_fields.py` module on the Numpy file, i.e. you don't have to re-process the output file.
If you want to create different plots you just need to edit and re-run the ``plot_fields.py`` module on the Numpy file, i.e. you don't have to re-process the output file.
.. figure:: ../../images_shared/antenna_like_GSSI_1500_patterns_E_Er5.png