# PMCX - Python bindings for Monte Carlo eXtreme photon transport simulator
- Copyright: (C) Matin Raayai Ardakani (2022-2023) <raayaiardakani.m at northeastern.edu>, Qianqian Fang (2019-2024) <q.fang at neu.edu>, Fan-Yu Yen (2023-2024) <yen.f at northeastern.edu>
- License: GNU Public License V3 or later
- Version: 0.3.5
- URL: https://pypi.org/project/pmcx/
- Github: https://github.com/fangq/mcx
\
\
This module provides a Python binding for Monte Carlo eXtreme (MCX).
For other binaries, including the standalone executable and the MATLAB bindings, see [our website](http://mcx.space).
Monte Carlo eXtreme (MCX) is a fast photon transport simulation software for 3D
heterogeneous turbid media. By taking advantage of the massively parallel
threads and extremely low memory latency in a modern graphics processing unit
(GPU), MCX is capable of performing Monte Carlo (MC) photon simulations at a
blazing speed, typically hundreds to a thousand times faster than a fully
optimized CPU-based MC implementation.
## How to Install
* PIP: ```pip install pmcx``` see https://pypi.org/project/pmcx/
## Runtime Dependencies
* **NVIDIA GPU Driver**: A CUDA-capable NVIDIA GPU and driver is required to run MCX. An up-to-date driver is recommended.
The binary wheel distributed over pip runs on NVIDIA drivers with CUDA 10.1 support on Windows, CUDA 9.2 support on Linux, and
CUDA 10.2 support on macOS, respectively. For more details on driver versions and their CUDA support, see the
[CUDA Release Notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html).
To download the latest driver for your system, see the
[NVIDIA Driver Download Page](https://www.nvidia.com/download/index.aspx).
**You shouldn't need to have CUDA toolkit installed**. MCX is built with the static CUDA runtime library.
* **Python**: Python 3.6 and newer is required. **Python 2 is not supported**.
* **numpy**: Used to pass/receive volumetric information to/from pmcx. To install, use either conda or pip
package managers: `pip install numpy` or `conda install numpy`
* (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**: Windows and Linux are fully supported; For building MCX on macOS, OSX 10.13 (High Sierra) and
older are highly recommended since 10.13 was the last version of macOS with NVIDIA CUDA support, and matching the CUDA
compiler version with the C/C++ compiler shipped with Xcode is easier. Newer macOS versions can be used for building MCX,
but need to have System Integrity Protection disabled prior to installing the CUDA toolkit due to the NVIDIA installer copying
its payload under the ```/Developer``` directory under root.
* **NVIDIA CUDA Toolkit**: CUDA 7.5 or newer is required. On macOS, 10.2 is the last available CUDA version.
For details on how to install CUDA, see the [CUDA Download Page](https://developer.nvidia.com/cuda-downloads).
The NVIDIA GPU driver of the target system must support the selected CUDA toolkit.
* **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**: CUDA Toolkit supports only the following compilers:
* GNU GCC for Linux-based distributions.
* Microsoft Visual Studio C/C++ Compiler for Windows.
* Apple Clang for macOS, available via Xcode. The last Xcode version supported by CUDA 10.2 is 10.3. If using an OSX
version higher than 10.15 it can be downloaded and installed from [Apple's Developer Website](https://developer.apple.com/download/)
with an Apple ID. After installation, select the proper Xcode version from the commandline, and set the ```SDKROOT```
environment variable:
```zsh
sudo xcode-select -s /Applications/Xcode_10.3.app/Contents/Developer/
export SDKROOT=/Applications/Xcode_10.3.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
```
Refer to each OS's online documentations for more in-depth information on how to install these compilers.
Note that the version of the C/C++ compiler used must be supported by the CUDA toolkit version. If not, compilation
will fail with an error notifying you of this problem. See the [CUDA Installation Guides](https://developer.nvidia.com/cuda-toolkit-archive)
for more details.
* **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:
```zsh
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.
Additionally, on Windows, make sure **Visual Studio's C++ CMake tools for Windows** is also installed by selecting its option
during installation.
### Build Steps
1. Ensure that ```cmake```, ```nvcc``` (NVIDIA CUDA Compiler) 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 ```pmcx/``` folder:
```bash
git clone --recursive https://github.com/fangq/mcx.git
cd mcx/pmcx
```
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 `pmcx-X.X.X-cpXX-cpXX-*.whl`
stored inside the `mcx/pmcx` folder. You can install this wheel package using `python3 -m pip install --force-reinstall pmcx-*.whl`
to force installing this locally compiled `pmcx` module and overwrite any previously installed versions.
## How to use
The PMCX module is easy to use. You can use the `pmcx.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 MCX simulation setting as positional
argument. The supported setting names are compatible to nearly all the input fields
for the MATLAB version of MCX - [MCXLAB](https://github.com/fangq/mcx/blob/master/mcxlab/mcxlab.m))
```python3
import pmcx
import numpy as np
import matplotlib.pyplot as plt
res = pmcx.run(nphoton=1000000, vol=np.ones([60, 60, 60], dtype='uint8'), 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,:, :]))
plt.show()
```
* Alternatively, one can also define a Python dict object containing each setting
as a key, and pass on the dict object to `pmcx.run()`, or preferably, `pmcx.mcxlab()`
```python3
import pmcx
import numpy as np
cfg = {'nphoton': 1000000, 'vol':np.ones([60,60,60],dtype='uint8'), '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 = pmcx.run(cfg) # pmcx.run returns detected photon data as a concatenated 2D array res['detp'], same for res['traj']
# or alternatively/preferably
res = pmcx.mcxlab(cfg) # pmcx.mcxlab calls pmcx.run, and postprocess res['detp'] and res['traj'] raw data into dict form
```
Raw data
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"_id": null,
"home_page": "https://github.com/fangq/mcx",
"name": "pmcx",
"maintainer": "Qianqian Fang",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "Monte Carlo simulation, Biophotonics, Ray-tracing, Rendering, GPU, Modeling, Biomedical Optics, Tissue Optics, Simulator, Optics, NVIDIA, CUDA",
"author": "Matin Raayai Ardakani, Qianqian Fang, Fan-Yu Yen",
"author_email": "raayaiardakani.m@northeastern.edu, q.fang@neu.edu, yen.f@northeastern.edu",
"download_url": "https://mcx.space",
"platform": null,
"description": "\r\n\r\n# PMCX - Python bindings for Monte Carlo eXtreme photon transport simulator\r\n\r\n- Copyright: (C) Matin Raayai Ardakani (2022-2023) <raayaiardakani.m at northeastern.edu>, Qianqian Fang (2019-2024) <q.fang at neu.edu>, Fan-Yu Yen (2023-2024) <yen.f at northeastern.edu>\r\n- License: GNU Public License V3 or later\r\n- Version: 0.3.5\r\n- URL: https://pypi.org/project/pmcx/\r\n- Github: https://github.com/fangq/mcx\r\n\r\n\\\r\n\\\r\n\r\n\r\nThis module provides a Python binding for Monte Carlo eXtreme (MCX).\r\nFor other binaries, including the standalone executable and the MATLAB bindings, see [our website](http://mcx.space).\r\n\r\nMonte Carlo eXtreme (MCX) is a fast photon transport simulation software for 3D \r\nheterogeneous turbid media. By taking advantage of the massively parallel \r\nthreads and extremely low memory latency in a modern graphics processing unit \r\n(GPU), MCX is capable of performing Monte Carlo (MC) photon simulations at a \r\nblazing speed, typically hundreds to a thousand times faster than a fully \r\noptimized CPU-based MC implementation.\r\n\r\n## How to Install\r\n\r\n* PIP: ```pip install pmcx``` see https://pypi.org/project/pmcx/\r\n\r\n## Runtime Dependencies\r\n* **NVIDIA GPU Driver**: A CUDA-capable NVIDIA GPU and driver is required to run MCX. An up-to-date driver is recommended.\r\nThe binary wheel distributed over pip runs on NVIDIA drivers with CUDA 10.1 support on Windows, CUDA 9.2 support on Linux, and\r\nCUDA 10.2 support on macOS, respectively. For more details on driver versions and their CUDA support, see the \r\n[CUDA Release Notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html). \r\nTo download the latest driver for your system, see the \r\n[NVIDIA Driver Download Page](https://www.nvidia.com/download/index.aspx).\r\n**You shouldn't need to have CUDA toolkit installed**. MCX is built with the static CUDA runtime library. \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 pmcx. To install, use either conda or pip \r\npackage managers: `pip install numpy` or `conda install numpy`\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**: Windows and Linux are fully supported; For building MCX on macOS, OSX 10.13 (High Sierra) and \r\nolder are highly recommended since 10.13 was the last version of macOS with NVIDIA CUDA support, and matching the CUDA \r\ncompiler version with the C/C++ compiler shipped with Xcode is easier. Newer macOS versions can be used for building MCX, \r\nbut need to have System Integrity Protection disabled prior to installing the CUDA toolkit due to the NVIDIA installer copying\r\nits payload under the ```/Developer``` directory under root.\r\n* **NVIDIA CUDA Toolkit**: CUDA 7.5 or newer is required. On macOS, 10.2 is the last available CUDA version.\r\nFor details on how to install CUDA, see the [CUDA Download Page](https://developer.nvidia.com/cuda-downloads). \r\nThe NVIDIA GPU driver of the target system must support the selected CUDA toolkit.\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**: CUDA Toolkit supports only the following compilers:\r\n * GNU GCC for Linux-based distributions.\r\n * Microsoft Visual Studio C/C++ Compiler for Windows.\r\n * Apple Clang for macOS, available via Xcode. The last Xcode version supported by CUDA 10.2 is 10.3. If using an OSX \r\n version higher than 10.15 it can be downloaded and installed from [Apple's Developer Website](https://developer.apple.com/download/) \r\n with an Apple ID. After installation, select the proper Xcode version from the commandline, and set the ```SDKROOT```\r\n environment variable:\r\n ```zsh\r\n sudo xcode-select -s /Applications/Xcode_10.3.app/Contents/Developer/\r\n export SDKROOT=/Applications/Xcode_10.3.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk\r\n ```\r\n \r\n Refer to each OS's online documentations for more in-depth information on how to install these compilers.\r\n Note that the version of the C/C++ compiler used must be supported by the CUDA toolkit version. If not, compilation\r\n will fail with an error notifying you of this problem. See the [CUDA Installation Guides](https://developer.nvidia.com/cuda-toolkit-archive)\r\n for more details.\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 ```zsh\r\n brew install libomp\r\n brew link --force libomp\r\n ```\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 Additionally, on Windows, make sure **Visual Studio's C++ CMake tools for Windows** is also installed by selecting its option\r\n during installation.\r\n\r\n### Build Steps\r\n1. Ensure that ```cmake```, ```nvcc``` (NVIDIA CUDA Compiler) 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 ```pmcx/``` folder:\r\n ```bash\r\n git clone --recursive https://github.com/fangq/mcx.git\r\n cd mcx/pmcx\r\n ```\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 `pmcx-X.X.X-cpXX-cpXX-*.whl`\r\nstored inside the `mcx/pmcx` folder. You can install this wheel package using `python3 -m pip install --force-reinstall pmcx-*.whl`\r\nto force installing this locally compiled `pmcx` module and overwrite any previously installed versions.\r\n\r\n\r\n## How to use\r\n\r\nThe PMCX module is easy to use. You can use the `pmcx.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 MCX simulation setting as positional\r\nargument. The supported setting names are compatible to nearly all the input fields\r\nfor the MATLAB version of MCX - [MCXLAB](https://github.com/fangq/mcx/blob/master/mcxlab/mcxlab.m))\r\n\r\n```python3\r\nimport pmcx\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nres = pmcx.run(nphoton=1000000, vol=np.ones([60, 60, 60], dtype='uint8'), 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,:, :]))\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 `pmcx.run()`, or preferably, `pmcx.mcxlab()`\r\n\r\n```python3\r\nimport pmcx\r\nimport numpy as np\r\ncfg = {'nphoton': 1000000, 'vol':np.ones([60,60,60],dtype='uint8'), '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 = pmcx.run(cfg) # pmcx.run returns detected photon data as a concatenated 2D array res['detp'], same for res['traj']\r\n# or alternatively/preferably\r\nres = pmcx.mcxlab(cfg) # pmcx.mcxlab calls pmcx.run, and postprocess res['detp'] and res['traj'] raw data into dict form\r\n```\r\n",
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