# PMMC - Python bindings for Mesh-based Monte Carlo (MMC) photon transport simulator
- Copyright: (C) Qianqian Fang (2025) <q.fang at neu.edu>
- License: GNU Public License V3 or later
- Version: 0.2.5
- URL: https://pypi.org/project/pmmc/
- Github: https://github.com/fangq/mmc
\
\
This module provides a Python binding for Mesh-based Monte Carlo (MMC).
For other binaries, including the standalone executable and the MATLAB bindings,
see [our website](https://mcx.space).
Mesh-based Monte Carlo (MMC) is a 3D Monte Carlo (MC) simulation software for
photon transport in complex turbid media. MMC combines the strengths of the
MC-based technique and the finite-element (FE) method: on the one hand, it
can handle general media, including low-scattering ones, as in the MC method;
on the other hand, it can use an FE-like tetrahedral mesh to represent curved
boundaries and complex structures, making it even more accurate, flexible,
and memory efficient. MMC uses the state-of-the-art ray-tracing techniques
to simulate photon propagation in a mesh space. It has been extensively
optimized for excellent computational efficiency and portability.
## How to Install
* PIP: ```pip install pmmc```, see https://pypi.org/project/pmmc/
## Runtime Dependencies
* **CPU or GPU**: An OpenCL-capable CPU or GPU; most modern CPUs or GPUs support OpenCL -
an industrial-standard heterogeneous computing library and specification (https://www.khronos.org/opencl/)
* **OpenCL CPU or GPU runtime/driver**: Both NVIDIA and AMD GPU graphics drivers should contain
out-of-box OpenCL runtimes or drivers; for Intel GPUs, one should install additional OpenCL runtime
support from https://github.com/intel/compute-runtime or install the `intel-opencl-icd` package
if the OS provides (such as Ubuntu 22.04); one can also install an open-source OpenCL runtime
[POCL](http://portablecl.org/), using package manager such as `sudo apt-get install pocl-opencl-icd`. However,
POCL's support is largely limited to CPUs. You **do not need** to install CUDA SDK to use pmmc.
* **Python**: Python 3.6 and newer is required. **Python 2 is not supported**.
* **numpy**: Used to pass/receive volumetric information to/from pmmc. To install, use either conda or pip
package managers: `pip install numpy` or `conda install numpy`
* **iso2mesh** is a easy-to-use mesh generator for creating the tetrahedral meshed domain
for pmmc, install it with `pip install iso2mesh`
* (optional) **jdata**: Only needed to read/write JNIfTI output files. To install, use pip: `pip install jdata`
on all operating systems; For Debian-based Linux distributions, you can also install to the system interpreter
using apt-get: `sudo apt-get install python3-jdata`. See https://pypi.org/project/jdata/ for more details.
* (optional) **bjdata**: Only needed to read/write BJData/UBJSON files. To install, run `pip install bjdata`
on all operating systems; For Debian-based Linux distributions, you can also install to the system interpreter
using apt-get: `sudo apt-get install python3-bjdata`. See https://pypi.org/project/bjdata/ for more details.
* (optional) **matplotlib**: For plotting the results. To install, run either `pip install matplotlib` or
`conda install matplotlib`
## Build Instructions
### Build Dependencies
* **Operating System**: pmmc and mmc can be compiled on most OSes, including Windows, Linux and MacOS.
* **OpenCL library**: compiling mmc or pmmc requires to link with `libOpenCL.so` on Linux, or `libOpenCL.dylib`
on MacOS or `OpenCL.dll` on Windows. These libraries should have been installed by either graphics driver or
OpenCL runtimes.
* **Python Interpreter**: Python 3.6 or above. The ```pip``` Python package manager and the ```wheel``` package (available
via ```pip```) are not required but recommended.
* **C/C++ Compiler**: pmmc can be compiled using a wide variety of C compilers, including
* GNU GCC for Linux, MacOS (intalled via MacPorts or brew), and Windows (installed via msys2, mingw64 or cygwin64)
* Microsoft Visual Studio C/C++ Compiler for Windows.
* Apple Clang for macOS, available via Xcode.
Refer to each OS's online documentations for more in-depth information on how to install these compilers.
MacOS provides built-in OpenCL library support.
* **OpenMP**: The installed C/C++ Compiler should have support for [OpenMP](https://www.openmp.org/).
GCC and Microsoft Visual Studio compiler support OpenMP out of the box. Apple Clang, however, requires manual
installation of OpenMP libraries for Apple Clang. The easiest way to do this is via the [Brew](https://brew.sh/) package
manager, preferably after selecting the correct Xcode version:
```bash
brew install libomp
brew link --force libomp
```
* **CMake**: CMake version 3.15 and later is required. Refer to the [CMake website](https://cmake.org/download/) for more information on how to download.
CMake is also widely available on package managers across all operating systems.
### Build Steps
1. Ensure that ```cmake```, ```python``` and the C/C++ compiler are all located over your ```PATH```.
This can be queried via ```echo $env:PATH``` on Windows or ```echo $PATH``` on Linux. If not, locate them and add their folder to the ```PATH```.
2. Clone the repository and switch to the ```pmmc/``` folder:
```bash
git clone --recursive https://github.com/fangq/mmc.git
cd mmc/pmmc
```
3. One can run `python3 setup.py install` or `python3 -m pip install .` to both locally build and install the module
4. If one only wants to locally build the module, one should run `python3 -m pip wheel .`
5. If the binary module is successfully built locally, you should see a binary wheel file `pmmc-X.X.X-cpXX-cpXX-*.whl`
stored inside the `mmc/pmmc` folder. You can install this wheel package using `python3 -m pip install --force-reinstall pmmc-*.whl`
to force installing this locally compiled `pmmc` module and overwrite any previously installed versions.
## How to use
The PMMC module is easy to use. You can use the `pmmc.gpuinfo()` function to first verify
if you have NVIDIA/CUDA compatible GPUs installed; if there are NVIDIA GPUs detected,
you can then call the `run()` function to launch a photon simulation.
A simulation can be defined conveniently in two approaches - a one-liner and a two-liner:
* For the one-liner, one simply pass on each MMC simulation setting as positional
argument. The supported setting names are compatible to nearly all the input fields
for the MATLAB version of MMC - [MMCLAB](https://github.com/fangq/mmc/blob/master/mmclab/mmclab.m))
```python3
import pmmc
import numpy as np
import matplotlib.pyplot as plt
import iso2mesh as i2m
node, face, elem = i2m.meshabox([0, 0, 0], [60, 60, 60], 10, 100) # create a mesh
gpus = pmmc.gpuinfo() # list all available GPUs
res = pmmc.run(nphoton=1000000, node=node, elem=elem, elemprop=np.ones(elem.shape[0]), tstart=0, tend=5e-9,
tstep=5e-9, srcpos=[30,30,0], srcdir=[0,0,1], prop=np.array([[0, 0, 1, 1], [0.005, 1, 0.01, 1.37]]))
res['flux'].shape
plt.imshow(np.log10(res['flux'][30,:, :].squeeze()))
plt.show()
```
* Alternatively, one can also define a Python dict object containing each setting
as a key, and pass on the dict object to `pmmc.run()`
```python3
import pmmc
import numpy as np
cfg = {'nphoton': 1000000, 'node': node, 'elem': elem, 'elemprop': np.ones(elem.shape[0]), 'tstart':0, 'tend':5e-9, 'tstep':5e-9,
'srcpos': [30,30,0], 'srcdir':[0,0,1], 'prop':[[0,0,1,1],[0.005,1,0.01,1.37]]}
res = pmmc.run(cfg)
```
Raw data
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"keywords": "Monte Carlo simulation, Biophotonics, Ray-tracing, Rendering, GPU, Modeling, Biomedical Optics, Tissue Optics, Simulator, Optics, OpenCL",
"author": "Qianqian Fang",
"author_email": "q.fang@neu.edu",
"download_url": "https://mcx.space",
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"description": "\r\n\r\n# PMMC - Python bindings for Mesh-based Monte Carlo (MMC) photon transport simulator\r\n\r\n- Copyright: (C) Qianqian Fang (2025) <q.fang at neu.edu>\r\n- License: GNU Public License V3 or later\r\n- Version: 0.2.5\r\n- URL: https://pypi.org/project/pmmc/\r\n- Github: https://github.com/fangq/mmc\r\n\r\n\\\r\n\\\r\n\r\n\r\nThis module provides a Python binding for Mesh-based Monte Carlo (MMC).\r\nFor other binaries, including the standalone executable and the MATLAB bindings,\r\nsee [our website](https://mcx.space).\r\n\r\nMesh-based Monte Carlo (MMC) is a 3D Monte Carlo (MC) simulation software for\r\nphoton transport in complex turbid media. MMC combines the strengths of the \r\nMC-based technique and the finite-element (FE) method: on the one hand, it \r\ncan handle general media, including low-scattering ones, as in the MC method; \r\non the other hand, it can use an FE-like tetrahedral mesh to represent curved \r\nboundaries and complex structures, making it even more accurate, flexible, \r\nand memory efficient. MMC uses the state-of-the-art ray-tracing techniques \r\nto simulate photon propagation in a mesh space. It has been extensively \r\noptimized for excellent computational efficiency and portability.\r\n\r\n\r\n## How to Install\r\n\r\n* PIP: ```pip install pmmc```, see https://pypi.org/project/pmmc/\r\n\r\n\r\n## Runtime Dependencies\r\n* **CPU or GPU**: An OpenCL-capable CPU or GPU; most modern CPUs or GPUs support OpenCL -\r\nan industrial-standard heterogeneous computing library and specification (https://www.khronos.org/opencl/)\r\n* **OpenCL CPU or GPU runtime/driver**: Both NVIDIA and AMD GPU graphics drivers should contain\r\nout-of-box OpenCL runtimes or drivers; for Intel GPUs, one should install additional OpenCL runtime\r\nsupport from https://github.com/intel/compute-runtime or install the `intel-opencl-icd` package\r\nif the OS provides (such as Ubuntu 22.04); one can also install an open-source OpenCL runtime\r\n[POCL](http://portablecl.org/), using package manager such as `sudo apt-get install pocl-opencl-icd`. However,\r\nPOCL's support is largely limited to CPUs. You **do not need** to install CUDA SDK to use pmmc.\r\n* **Python**: Python 3.6 and newer is required. **Python 2 is not supported**.\r\n* **numpy**: Used to pass/receive volumetric information to/from pmmc. To install, use either conda or pip \r\npackage managers: `pip install numpy` or `conda install numpy`\r\n* **iso2mesh** is a easy-to-use mesh generator for creating the tetrahedral meshed domain\r\nfor pmmc, install it with `pip install iso2mesh`\r\n* (optional) **jdata**: Only needed to read/write JNIfTI output files. To install, use pip: `pip install jdata` \r\non all operating systems; For Debian-based Linux distributions, you can also install to the system interpreter \r\nusing apt-get: `sudo apt-get install python3-jdata`. See https://pypi.org/project/jdata/ for more details. \r\n* (optional) **bjdata**: Only needed to read/write BJData/UBJSON files. To install, run `pip install bjdata` \r\non all operating systems; For Debian-based Linux distributions, you can also install to the system interpreter \r\nusing apt-get: `sudo apt-get install python3-bjdata`. See https://pypi.org/project/bjdata/ for more details. \r\n* (optional) **matplotlib**: For plotting the results. To install, run either `pip install matplotlib` or\r\n`conda install matplotlib`\r\n\r\n## Build Instructions\r\n\r\n### Build Dependencies\r\n* **Operating System**: pmmc and mmc can be compiled on most OSes, including Windows, Linux and MacOS.\r\n* **OpenCL library**: compiling mmc or pmmc requires to link with `libOpenCL.so` on Linux, or `libOpenCL.dylib`\r\non MacOS or `OpenCL.dll` on Windows. These libraries should have been installed by either graphics driver or\r\nOpenCL runtimes.\r\n* **Python Interpreter**: Python 3.6 or above. The ```pip``` Python package manager and the ```wheel``` package (available\r\n via ```pip```) are not required but recommended.\r\n* **C/C++ Compiler**: pmmc can be compiled using a wide variety of C compilers, including\r\n * GNU GCC for Linux, MacOS (intalled via MacPorts or brew), and Windows (installed via msys2, mingw64 or cygwin64)\r\n * Microsoft Visual Studio C/C++ Compiler for Windows.\r\n * Apple Clang for macOS, available via Xcode.\r\n\r\n Refer to each OS's online documentations for more in-depth information on how to install these compilers.\r\n MacOS provides built-in OpenCL library support.\r\n* **OpenMP**: The installed C/C++ Compiler should have support for [OpenMP](https://www.openmp.org/). \r\n GCC and Microsoft Visual Studio compiler support OpenMP out of the box. Apple Clang, however, requires manual \r\n installation of OpenMP libraries for Apple Clang. The easiest way to do this is via the [Brew](https://brew.sh/) package\r\n manager, preferably after selecting the correct Xcode version:\r\n```bash\r\nbrew install libomp\r\nbrew link --force libomp\r\n```\r\n* **CMake**: CMake version 3.15 and later is required. Refer to the [CMake website](https://cmake.org/download/) for more information on how to download.\r\n CMake is also widely available on package managers across all operating systems.\r\n\r\n### Build Steps\r\n1. Ensure that ```cmake```, ```python``` and the C/C++ compiler are all located over your ```PATH```.\r\nThis can be queried via ```echo $env:PATH``` on Windows or ```echo $PATH``` on Linux. If not, locate them and add their folder to the ```PATH```.\r\n\r\n2. Clone the repository and switch to the ```pmmc/``` folder:\r\n```bash\r\ngit clone --recursive https://github.com/fangq/mmc.git\r\ncd mmc/pmmc\r\n```\r\n3. One can run `python3 setup.py install` or `python3 -m pip install .` to both locally build and install the module\r\n\r\n4. If one only wants to locally build the module, one should run `python3 -m pip wheel .`\r\n\r\n5. If the binary module is successfully built locally, you should see a binary wheel file `pmmc-X.X.X-cpXX-cpXX-*.whl`\r\nstored inside the `mmc/pmmc` folder. You can install this wheel package using `python3 -m pip install --force-reinstall pmmc-*.whl`\r\nto force installing this locally compiled `pmmc` module and overwrite any previously installed versions.\r\n\r\n\r\n## How to use\r\n\r\nThe PMMC module is easy to use. You can use the `pmmc.gpuinfo()` function to first verify\r\nif you have NVIDIA/CUDA compatible GPUs installed; if there are NVIDIA GPUs detected,\r\nyou can then call the `run()` function to launch a photon simulation.\r\n\r\nA simulation can be defined conveniently in two approaches - a one-liner and a two-liner:\r\n\r\n* For the one-liner, one simply pass on each MMC simulation setting as positional\r\nargument. The supported setting names are compatible to nearly all the input fields\r\nfor the MATLAB version of MMC - [MMCLAB](https://github.com/fangq/mmc/blob/master/mmclab/mmclab.m))\r\n\r\n```python3\r\nimport pmmc\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimport iso2mesh as i2m\r\nnode, face, elem = i2m.meshabox([0, 0, 0], [60, 60, 60], 10, 100) # create a mesh\r\n\r\ngpus = pmmc.gpuinfo() # list all available GPUs\r\n\r\nres = pmmc.run(nphoton=1000000, node=node, elem=elem, elemprop=np.ones(elem.shape[0]), tstart=0, tend=5e-9,\r\n tstep=5e-9, srcpos=[30,30,0], srcdir=[0,0,1], prop=np.array([[0, 0, 1, 1], [0.005, 1, 0.01, 1.37]]))\r\nres['flux'].shape\r\n\r\nplt.imshow(np.log10(res['flux'][30,:, :].squeeze()))\r\nplt.show()\r\n```\r\n\r\n* Alternatively, one can also define a Python dict object containing each setting\r\nas a key, and pass on the dict object to `pmmc.run()`\r\n\r\n```python3\r\nimport pmmc\r\nimport numpy as np\r\ncfg = {'nphoton': 1000000, 'node': node, 'elem': elem, 'elemprop': np.ones(elem.shape[0]), 'tstart':0, 'tend':5e-9, 'tstep':5e-9,\r\n 'srcpos': [30,30,0], 'srcdir':[0,0,1], 'prop':[[0,0,1,1],[0.005,1,0.01,1.37]]}\r\nres = pmmc.run(cfg)\r\n```\r\n",
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