Sph_ Mac OS
Installing the dependencies on Mac OS X ¶ On OS X, your best bet is to install Enthought Canopy or Anaconda or some other Python distribution. Ensure that you have gcc or clang installed by installing XCode. See this if you installed XCode but can’t find clang or gcc. Global Nav Open Menu Global Nav Close Menu; Apple; Shopping Bag +.
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AQUAgpusph
- GitHub - Xiangyu-Hu/SPHinXsys: SPHinXsys (pronunciation: s'finksis) is an acronym from Smoothed Particle Hydrodynamics for industrial compleX systems. It provides C APIs for physical accurate simulation and aims to model coupled industrial dynamic systems including fluid, solid, multi-body dynamics and beyond with SPH (smoothed particle hydrodynamics), a meshless computational method using particle discretization.
- Troubleshooting of CCI PC hardware (to include laptops, desktops and accessories); Operating System (Windows 10) and Various Software Applications (Office 365, Adobe, Web Browsers, etc.) Troubleshooting of Apple Hardware (to include laptops, desktops and accessories) also to include Operating System (Mac OS) and Various Software Applications.
- Mac OS: Mac OS X 10.6, Mac OS X 10.5, Mac OS X 10.4 (Intel) Safari 4.0 and above, Mozilla Firefox 3.0 and above, Google Chrome 2.0 and above3, Opera 9.5 and above, AOL Desktop for Mac 1.0 and above: Linux: Red Hat® Enterprise Linux (RHEL) 5 or later, openSUSE® 11 or later, Ubuntu 9.10 or later: Mozilla Firefox 3.0 and above, Google Chrome 2.0.
AQUAgpusph is a free SPH solver developed by the CEHINAV group. It is accelerated with OpenCL and has a wide variety of boundary conditions. It is easily modable and extensible with Python scripts.
DualSPHysics
DualSPHysics code was developed to use SPH for real engineering problems withsoftware that can be run on either CPUs or GPUs (graphics cards with powerfulparallel computing). GPUs offer now a higher computing power than CPUs and theyare an affordable option to accelerate SPH with a low economic cost. Thereby,the simulations can be performed using a GPU card installed on a personalcomputer. DualSPHysics is open source and can be freely downloaded from www.dual.sphysics.org. It has been mainly developed by researchers from the University of Vigo (Spain) and the University of Manchester (U.K.).
GADGET
GADGET is a freely available code for cosmological N-body/SPH simulations on massively parallel computers with distributed memory. GADGET uses an explicit communication model that is implemented with the standardized MPI communication interface. The code can be run on essentially all supercomputer systems presently in use, including clusters of workstations or individual PCs.
GPUSPH
GPUSPH is a GPU accelerated open-source code for use on Nvidia CUDA enabled graphics cards. It is available for Linux/Unix/OS X users at http://www.gpusph.org along with a 'manual' for installation instructions. The open-source nature of the code means that you can develop your own problems with the code. It is licensed under the GPL copyright. Shortly the code will also be available through the Inundation Science & Engineering Cooperative (http://isec.nacse.org), with a discussion forum for developers.
IMPETUS
IMPETUS Afea Solver is a software package for non-linear computational mechanics. It is primarily developed to predict large deformations of structures and components exposed to extreme loading conditions. The package comprises different solver modules and a Post Processor. Our guiding principles are accuracy, robustness and simplicity for the user. The number of purely numerical parameters that the user has to provide as input is kept at a minimum. IMPETUS Afea Solver is adapted to the GPU-technology. Utilizing the computational force of a potent graphics card can considerably speed up your calculations. For more information or requests please contact sales@impetus-afea.com. Website: http://www.impetus-afea.com.
LS-DYNA
LS-DYNA LS-DYNA is an advanced general-purpose multiphysics simulation software package that is actively developed by the Livermore Software Technology Corporation (LSTC).
ndspmhd
A 'developmental' 1-, 2- and 3-dimensional Smoothed Particle Hydrodynamics (SPH) simulation code used to develop new SPH algorithms. It contains implementations of nearly all algorithms published for SPH by Price (without optimisation or parallelisation), which are good for exploring how SPH works and trying out new ideas on a wide range of test problems for compressible hydrodynamics and magnetohydrodynamics, mostly in the context of astrophysics. Website: http://users.monash.edu.au/~dprice/ndspmhd.
Neutrino
Neutrino is an incompressible SPH solver with an GUI and visualization capabilities. It is currently used at Idaho National Labs and other universities to do risk analysis for flooding related scenarios and for modeling nuclear reactor dynamics for heat exchange and boiling. The license is free for non-commercial / academic usage. The web site is: http://www.centroidlab.com/neutrinodynamics.
PASIMODO
Pasimodo is a versatile academic and commercial numerical simulation platform for the simulation of granular materials, fluids, largely deformable solids, and other simulation tasks. The software is developed by the Institute of Engineering and Computational Mechanics (ITM) of the University of Stuttgart, Germany in cooperation with the software development company Inpartik. As both a research code and commercial software, Pasimodo combines the advantages of accuracy, robustness, and simplicity for the user expected from a commercial program. With one-, two- or three-dimensional, serial or parallel simulations with static or dynamic resolution, it has been successfully applied in numerous academic and industrial projectsincluding sloshing fluids in tanks or solids in cutting processes. Contacts are: Peter Eberhard (ITM, peter.eberhard@itm.uni-stuttgart.de) and Florian Fleissner (Inpartik, florian.fleissner@inpartik.com). For further information on Pasimodo and some example videos please visit http://www.itm.uni-stuttgart.de/research/pasimodo/pasimodo_en.php.
PersianSPH
PersianSPH is a free open source code based on SPH and it has been developed in C++ on Ubuntu. It is a Multi-physics platform, capable of coupling the dynamics of soils, fluids and structures in 2D and 3D. It includes elastic-plastic soil & structure behaviour, Newtonian and non-Newtonian fluids, and coupled interactions such as seepage flow. It is suitable for simulating post-failure behaviour, large deformation, rheology of solid materials, and particularly soil-water interaction.
PreonLab
PreonLabis an advanced commercial engineering software developed by FIFTY2 Technology. Its capable of simulating large variety of materials, including non-Newtonian fluids and snow. It employs an implicit incompressible SPH (IISPH) solver and uses a state-of-the-art hybrid parallelization strategy. Additionally, PreonLab has in-situ postprocessing capability, including a ray tracer to which enables realistic rendering of results.
Please visit https://www.fifty2.eu/preonlab/ for more details.
PySPH
PySPH is an open-source framework for Smoothed Particle Hydrodynamics (SPH) simulations. It is implemented in Python and the performance-critical parts are implemented in Cython and PyOpenCL. PySPH allows users to write their high-level code in pure Python. This Python code is automatically converted to high-performance Cython or OpenCL which is compiled and executed. PySPH can also be configured to work seamlessly with OpenMP, OpenCL, and MPI without making any change to the simulation code. The latest software is available at https://github.com/pypr/pysph and documentation for PySPH is available at http://pysph.readthedocs.org/.
SPHERA
SPHERA v.9.0.0 (RSE SpA) is an SPH FOSS (Free/Libre and Open-Source Software) research code. SPHERA v.9.0.0 is featured by two alternative boundary treatment schemes (based on either volume integrals or discrete surface elements), a scheme for body transport in fluid flows, a scheme for dense granular flows and a scheme for erosion criterion. SPHERA has been validated on the following application fields: several types of floods (with transport of solid bodies, bed-load transport, flood-control works, flood-induced damage; domain spatial coverage of some hundreds of squared kilometres) and landslides, sloshing tanks, sea waves and sediment removal from water reservoirs. SPHERA is developed on GitHub at https://github.com/AndreaAmicarelliRSE/SPHERA.
SPH-flow
SPH-flow is one of the most advanced SPH solver dedicated to complex physics simulation developed by Ecole Centrale Nantes and NEXTFLOW Software (http://www.nextflow-software.com/). NEXTFLOW Software is a company specialized in simulation solutions and services with a team of experts in development, integration and implementation. From car makers to suppliers, from marine contractors to aerospace engineering offices, SPH-flow technology has been successfully applied on numerous projects. For further information and SPH-flow licenses request, please use NEXTFLOW Software contact mailbox.
SPHinXsys
SPHinXsys is an acronym from Smoothed Particle Hydrodynamics for industrial compleXsystems. It provides C++ APIs for physical accurate simulation and aims to model coupled industrial dynamic systems including fluid, solid, multi-body dynamics and beyond with SPH (smoothed particle hydrodynamics), a meshless computational method using particle discretization.
Link: https://github.com/Xiangyu-Hu/SPHinXsys
SPHysics
SPHYSICS - This is a free open-source SPH code that released 2007 developed jointly by researchers at the Johns Hopkins University (U.S.A.), the University of Vigo (Spain), the University of Manchester (U.K.) and the University of Rome La Sapienza (Italy). The 2-D & 3-D code has been developed specifically for free-surface hydrodynamics. The code now includes serial, parallel and GPU versions.
SPLASH
SPLASH is a publicly available visualisation tool for Smoothed Particle Hydrodynamics simulations developed over a number of years, and can be used to read, convert and visualise output from all known publicly available SPH codes.
SPlisHSPlasH
SPlisHSPlasHis an active open-source research project which implements many state-of-the-art SPH methods and is continuously being optimized using OpenMP parallelization, AVX vectorization and CUDA. The library implements several current explicit and implicit pressure solvers, rigid-fluid coupling with static and dynamic bodies, multiple explicit and implicit viscosity solvers, as well as different approaches for the simulation of surface tension, vorticity, elasticity, drag forces and multiphase flow.
SPlisHSPlasH is written in C++ and provides a C++ interface as well as a Python interface for simple extensibility.
The latest version is available at https://github.com/InteractiveComputerGraphics/SPlisHSPlasH.
SWIFT
SWIFT(SPH With Inter-dependent Fine-grained Tasking) is a fully open-source (LGPL v3) SPH code dedicated to massively parallel cosmological and astrophysical simulations.SWIFT is based on new parallelization paradigms such as task-based parallelism, asynchronous MPI communications and SIMD vectorization to fully exploit modern HPC architectures. Simulations with more than 10^11 particles on 10^5 compute cores have been performed. SWIFT implements many flavours of compressible SPH and is coupled to an N-body solver for gravity. The code is mainly developed by researchers at the university of Leiden (Netherlands) and Durham (UK). Many examples are provided alongside the code to help users get started.
To install PySPH, you need a working Python environment with the requireddependencies installed. You may use any of the available Python distributions.Note that PySPH is currently tested with Python-2.x and not with 3.x (yet). Ifyou are new to Python we recommend Enthought Canopy. PySPH will workfine with Anaconda or other environments like WinPython. The followinginstructions should help you get started.
Since there is a lot of information here, we suggest that you skim the sectionon Dependencies and then directly jump to one of the “Installing thedependencies on xxx” sections below depending on your operating system.Depending on your chosen Python distribution, simply follow the instructionsand links referred therein.
Core dependencies¶
The core dependencies are:
- Cython (version 0.20 and above)
- nose for running the unit tests.
These packages can be installed from your Python distribution’s packagemanager, or using pip or easy_install. For more detailed instructions onhow to do this for different distributions, see below.
Running PySPH requires a working C/C++ compiler on your machine. On Linux/OS Xthe gcc toolchain will work well. On Windows, you will need to have MicrosoftVisual C++ Compiler for Python 2.7 or anequivalent compiler. More details are available below.
Note
PySPH generates high-performance code and compiles it on the fly. Thisrequires a working C/C++ compiler even after installing PySPH.
Optional dependencies¶
The optional dependencies are:
- OpenMP: PySPH can use OpenMP if it is available. Installation instructionsare available below.
- Mayavi: PySPH provides a convenient viewer to visualize the output ofsimulations. This viewer is called
pysph_viewerand requires Mayavi tobe installed. Since this is only a viewer it is optional for use, however,it is highly recommended that you have it installed as the viewer is veryconvenient. - mpi4py and Zoltan: If you want to use PySPH in parallel, you will needmpi4py and the Zoltan data management library. PySPH will work in serialwithout mpi4py or Zoltan. Simple build instructions for Zoltan areincluded below.
Mayavi is packaged with all the major distributions and is easy to install.Zoltan is very unlikely to be already packaged and will need to be compiled.
Building and linking PyZoltan on OSX/Linux¶
We’ve provided a simple Zoltan build script in the repository. This works onLinux and OS X but not on Windows. It can be used as:
where the INSTALL_PREFIX is where the library and includes will beinstalled. You may edit and tweak the build to suit your installation.However, this script is what we use to build Zoltan on our continuousintegration servers on Drone and Shippable.
After Zoltan is build, set the environment variable ZOLTAN to point to theINSTALL_PREFIX that you used above:
Note that replace $INSTALL_PREFIX with the directory you specified above.After this, follow the instructions to build PySPH. The PyZoltan wrappers willbe compiled and available.
Note
The installation will use $ZOLTAN/include and $ZOLTAN/lib to findthe actual directories, if these do not work for your particularinstallation for whatever reason, set the environment variablesZOLTAN_INCLUDE and ZOLTAN_LIBRARY explicitly without setting upZOLTAN. If you used the above script, this would be:
GNU/Linux is probably the easiest platform to install PySPH. On Ubuntu one mayinstall the dependencies using:
OpenMP is typically available but can be installed with:
If you need parallel support:
On Linux it is probably best to install PySPH into its own virtualenvironment. This will allow you to install PySPH as a user without anysuperuser priviledges. See the section below on Using a virtualenv for PySPH. Inshort do the following:
You should be set now and should skip to Downloading PySPH andBuilding and Installing PySPH.
If you are using Enthought Canopy or Anaconda the instructions in thesection Installing the dependencies on Mac OS X will be useful as the instructions aresimilar.
Note
If you wish to see a working build/test script please see ourshippable.yml. Or youcould see the build script hosted at Drone.io.
On OS X, your best bet is to install Enthought Canopy or Anaconda or someother Python distribution. Ensure that you have gcc or clang installed byinstalling XCode. See thisif you installed XCode but can’t find clang or gcc.
OpenMP on OSX¶
Sph_ Mac Os 11
If you need to use OpenMP, the default clang compiler on OSX does not supportit. There are some experimental versions available. One easy to installoption is to use brew to install gcc. For example you can try:
The build may not see omp.h and you can work around this by manuallylinking to it like so (modify this to suit your installation):
Once this is done, you need to use this as your default compiler, you can tellthe Python to use this by setting:
Using Canopy¶
Download the Canopy express installer for your platform (the full installer isalso fine). Launch Canopy after you install it so it initializes your userenvironment. If you have made Canopy your default Python, all should be well,otherwise launch the Canopy terminal from the Tools menu of the Canopy editorbefore typing your commands below.
NumPy ships by default but Cython does not. Mako and Cython can be installedwith pip easily (pip will be available in your Canopy environment):
Mayavi is best installed with the Canopy package manager:
Note
If you are a subscriber you can also enpkgcython to installEnthought’s build.
If you need parallel support, please see Installing mpi4py and Zoltan on OS X, otherwise,skip to Downloading PySPH and Building and Installing PySPH.
Using Anaconda¶
After installing Anaconda, you will need to make sure the dependencies areinstalled:
If you need parallel support, please see Installing mpi4py and Zoltan on OS X, otherwise,skip to Downloading PySPH and Building and Installing PySPH.
Installing mpi4py and Zoltan on OS X¶
In order to build/install mpi4py one first has to install the MPI library.This is easily done with Homebrew as follows (you need to have brewinstalled for this but that is relatively easy to do):
After this is done, one can install mpi4py by hand. First download mpi4pyfrom here. Then run the following(modify these to suit your XCode installation and version of mpi4py):
Change the above environment variables to suite your SDK version. If thisinstalls correctly, mpi4py should be available. You can now build Zoltan,(the script to do this is in the pysph sources, see Downloading PySPH)
You should be set now and should move to Building and Installing PySPH.
While it should be possible to use mpi4py and Zoltan on Windows, we do not atthis point have much experience with this. Feel free to experiment and let usknow if you’d like to share your instructions. The following instructionsare all without parallel support.
Using Canopy¶
Sph_ Mac Os Catalina
Download and install Canopy Express for you Windows machine (32 or 64 bit).Launch the Canopy editor at least once so it sets up your user environment.Make the Canopy Python the default Python when it prompts you. If you havealready skipped that option, you may enable it in the Edit->Preferencesmenu. With that done you may install the required dependencies. You caneither use the Canopy package manager or use the command line. We will usethe command line for the rest of the instructions. To start a command line,click on “Start” and navigate to the AllPrograms/EnthoughtCanopy menu.Select the “Canopy command prompt”, if you made Canopy your default Python,just starting a command prompt (via cmd.exe) will also work.
On the command prompt, Mako and Cython can be installed with pip easily(pip should be available in your Canopy environment):
Mayavi is best installed with the Canopy package manager:
Once you are done with this, please skip ahead toInstalling Visual C++ Compiler for Python 2.7.
Note
If you are a subscriber you can also enpkgcython to installEnthought’s build.
Using WinPython¶
Instead of Canopy or Anaconda you could try WinPython 2.7.x.x. To obtain thecore dependencies, download the corresponding binaries from Christoph Gohlke’sUnofficial Windows Binaries for Python Extension Packages. Mayavi is available throughthe binary ETS.
You can now add these binaries to your WinPython installation by going toWinPython Control Panel. The option to add packages is available under thesection Install/upgrade packages.
Make sure to set your system PATH variable pointing to the location of thescripts as required. If you have installed WinPython 2.7.6 64-bit, make sureto set your system PATH variables to <pathtoinstallationfolder>/python-2.7.6.amd64 and <pathtoinstallationfolder>/python-2.7.6.amd64/Scripts/.
Once you are done with this, please skip ahead toInstalling Visual C++ Compiler for Python 2.7.
Using Anaconda¶
Install Anaconda for your platform, make it the default and then install therequired dependencies:
Once you are done with this, please skip ahead toInstalling Visual C++ Compiler for Python 2.7.
Installing Visual C++ Compiler for Python 2.7¶
For all of the above Python distributions, it is highly recommended that youbuild PySPH with Microsoft’s Visual C++ for Python 2.7. Werecommend that you download and install the VCForPython27.msi availablefrom the link. Make sureyou install the system requirements specified on that page. For example, onWindows 7 you will need to install the Microsoft Visual C++ 2008 SP1Redistributable Package for your platform (x86 for 32 bit or x64 for 64 bit)and on Windows 8 you will need to install the .NET framework 3.5. Please lookat the link given above, it should be fairly straightforward. Note that doingthis will also get OpenMP working for you.
After you do this, you will find a “Microsoft Visual C++ Compiler Package forPython 2.7” in your Start menu. Choose a suitable command prompt from thismenu for your architecture and start it (we will call this the MSVC commandprompt). You may make a short cut to it as you will need to use this commandprompt to build PySPH and also run any of the examples.
See section Downloading PySPH and get a copy of PySPH. On the MSVCcommand prompt, do the following (replace the pysph with the path to whereyou have downloaded and unzipped the pysph sources):
One this is done, you may follow section Building and Installing PySPH. You maynotice that the windows_env.bat convenience batch file merely sets uptwo environment variables:
If you are using pip to directly install PySPH you will need to set thesemanually.
Warning
On 64 bit Windows, do not build PySPH with mingw64 as it does not workreliably at all and frequently crashes. YMMV with mingw32 but it is saferand just as easy to use the MS VC++ compiler.
A virtualenv allows you to create an isolated environment for PySPH and itsrelated packages. This is useful in a variety of situations.
- Your OS does not provide a recent enough Cython version (say you arerunning Debian stable).
- You do not have root access to install any packages PySPH requires.
- You do not want to mess up your system files and wish to localizeany installations inside directories you control.
- You wish to use other packages with conflicting requirements.
- You want PySPH and its related packages to be in an “isolated” environment.
You can either install virtualenv (or ask your system administrator to) orjust download the virtualenv.py script and useit (run pythonvirtualenv.py after you download the script).
Create a virtualenv like so:
This creates a directory called pysph_env which contains all the relevantfiles for your virtualenv, this includes any new packages you wish to installinto it. You can delete this directory if you don’t want it anymore for somereason. This virtualenv will also “inherit” packages from your system. Henceif your system administrator already installed NumPy it may be imported fromyour virtual environment and you do not need to install it. This isvery useful for large packages like Mayavi, Qt etc.
Sph_ Mac Os Download
Note
If your version of virtualenv does not support the--system-site-packages option, please use the virtualenv.py scriptmentioned above.
Once you create a virtualenv you can activate it as follows (on a bashshell):
On Windows you run a bat file as follows:
This sets up the PATH to point to your virtualenv’s Python. You may now runany normal Python commands and it will use your virtualenv’s Python. Forexample you can do the following:
Now PySPH will be installed into myenv. You may deactivate yourvirtualenv using the deactivate command:
On Windows, use myenvScriptsactivate.bat andmyenvScriptsdeactivate.bat.
If for whatever reason you wish to delete myenv just remove the entiredirectory:
Note
With a virtualenv, one should be careful while running things likeipython or nosetests as these are sometimes also installed on thesystem in /usr/bin. If you suspect that you are not running thecorrect Python, you could simply run (on Linux/OS X):
to be absolutely sure.
Using Virtualenv on Canopy¶
If you are using Enthought Canopy, it already bundles virtualenv for you butyou should use the venv script. For example:
The rest of the steps are the same as above.
One way to install PySPH is to use pip
This will install PySPH, and you should be able to import it and use themodules with your Python scripts that use PySPH. This will not give youaccess to the examples. The best way to get those is to get PySPH fromgit or download a tarball or ZIP as described below.
To get PySPH using git type the following
If you do not have git or do not wish to bother with it, you can get a ZIP ortarball from the pysph site.You can unzip/untar this and use the sources.
In the instructions, we assume that you have the pysph sources in thedirectory pysph and are inside the root of this directory. For example:
or if you cloned the repository:
Once you have downloaded PySPH you should be ready to build and install it,see Building and Installing PySPH.
Once you have the dependencies installed you can install PySPH with:
If you downloaded PySPH using git or used a tarball you can do:
You could also do:
This is useful if you are tracking the latest version of PySPH via git. Withgit you can update the sources and rebuild using:
You should be all set now and should next consider Running the tests.
If you installed PySPH using pythonsetup.pydevelop you can run the tests as:
If you installed PySPH using pythonsetup.pyinstall you should run thetests as:
This is because nosetests will incorrectly will pick up the local pysphpackages instead of the installed version. The -w option just changes theactive directory to docs. Alternatively, change directory to some otherdirectory that does not contain the directory pysph in it and run the firstcommand above.
If you see errors you might want more verbose reporting which you can getwith:
This should run all the tests that do not take a long while to complete. Ifthis fails, please contact the pysph-users mailing list or send us email.
Once you run the tests, you should see the section onRunning the examples.
Note
We use python-mnose.core instead of nosetests as this ensuresthat the right Python executable is used. nostests is sometimesinstalled in the system in /usr/bin/nosetests and running that wouldpick up the system Python instead of the one in a virtualenv. Thisresults in incorrect test errors leading to confusion.
You can verify the installation by exploring some examples:
Try this:
to see the different options supported by each example. You can view the datagenerated by the simulation (after the simulation is complete or during thesimulation) by running the pysph_viewer application. To view thesimulated data you may do:
If you have Mayavi installed this should show a UI that looks like:
There are other examples like those in the transport_velocity directory:
This runs the driven cavity problem using the transport velocity formulationof Adami et al. You can verify the results for this problem using the helperscript examples/transport_velocity/ldcavity_results.py to plot, for examplethe streamlines:
If you want to use PySPH for elastic dynamics, you can try some of theexamples from Gray et al., Comput. Methods Appl. Mech. Engrg. 190(2001), 6641-6662:
Which runs the problem of the collision of two elastic rings:
The auto-generated code for the example resides in the directory~/.pysph/source. A note of caution however, it’s not for the faint hearted.
Running the examples with OpenMP¶
If you have OpenMP available run any of the examples as follows:
This should run faster if you have multiple cores on your machine. Ifyou wish to change the number of threads to run simultaneously, you cantry the following:
You may need to set the number of threads to about 4 times the number ofphysical cores on your machine to obtain the most scale-up. If you wishto time the actual scale up of the code with and without OpenMP you maywant to disable any output (which will be serial), you can do thislike:
Running the examples with MPI¶
If you compiled PySPH with Zoltan and have mpi4py installed you may run anyof the examples with MPI as follows:
This may not give you significant speedup if the problem is too small. You canalso combine OpenMP and MPI if you wish. You should take care to setup the MPIhost information suitably to utilize the processors effectively.
PySPH is organized into several sub-packages. These are:
pysph.base: This subpackage defines thepysph.base.particle_array.ParticleArray,pysph.base.carray.CArray(which are used by the particlearrays), the various SPH Kernels, the nearest neighborparticle search (NNPS) code, and the Cython code generation utilities.pysph.sph: Contains the various SPH equations, theIntegrator module and associated integration steppers, and thecode generation for the SPH looping.pysph.sph.wccontains theequations for the weakly compressible formulation.pysph.sph.solid_mechcontains the equations for solid mechanics andpysph.sph.mischas miscellaneous equations.pysph.solver: Provides thepysph.solver.solver.Solver, thepysph.solver.application.Applicationand a convenient way tointeract with the solver as it is running.pysph.parallel: Provides the parallel functionality.pysph.tools: Provides some useful tools including thepysph_viewerwhich is based on Mayavi.