Adding patterns

antenna_like_GSSI_1500_patterns_E_rxsorigin.txt

@@ -0,0 +1,3 @@
+0.125000
+0.190000
+0.190000

docs/source/accelerators.rst

@@ -1,8 +1,8 @@
 .. _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.
 
@@ -17,7 +17,7 @@ Some of these accelerators and frameworks require additional software to be inst
 .. 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`.
+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 :code:`conda activate gprMax`. Run :code:`pip install pycuda`
+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
@@ -111,4 +111,4 @@ For example, to run a B-scan that contains 60 A-scans (traces) on a system with
 
 .. 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.

docs/source/hpc.rst

@@ -1,8 +1,8 @@
 .. _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.
 

examples/antenna_like_GSSI_1500_patterns.py

@@ -0,0 +1,84 @@
+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)

examples/antenna_like_GSSI_1500_patterns_H

@@ -0,0 +1,48 @@
+#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:

gprMax/cmds_multiuse.py

@@ -1372,6 +1372,7 @@ class SoilPeplinski(UserObjectMulti):
                                 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):

toolboxes/AntennaPatterns/README.rst

@@ -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.
+* ``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.
+* ``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_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_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.
+* 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.
+* 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`
+* 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