# Monte Carlo eXtreme  (MCX-CL) - OpenCL Edition

- **Author:** Qianqian Fang (q.fang at neu.edu)
- **License:** GNU General Public License version 3 (GPLv3)
- **Version:** 1.6 (Harmonic)
- **Website:** [ https://mcx.space]( https://mcx.space)

![Mex and Binaries](https://github.com/fangq/mcxcl/actions/workflows/build_all.yml/badge.svg)\
![Linux Python Module](https://github.com/fangq/mcxcl/actions/workflows/build_linux_manywheel.yml/badge.svg)\
![MacOS Python Module](https://github.com/fangq/mcxcl/actions/workflows/build_macos_wheel.yml/badge.svg)\
![Windows Python Module](https://github.com/fangq/mcxcl/actions/workflows/build_windows_wheel.yml/badge.svg)


Table of Contents:
-------------

  * [What's New](#whats-new)
  * [Introduction](#introduction)
  * [Requirement and Installation](#requirement-and-installation)
    + [Step 1. Verify your CPU/GPU support](#step-1-verify-your-cpugpu-support)
    + [Step 2. Install MATLAB or GNU Octave](#step-2-install-matlab-or-gnu-octave)
    + [Step 3. Installing MCXCL](#step-3-installing-mcxcl)
    + [Step 4. Start MCXStudio and query GPU information](#step-4-start-mcxstudio-and-query-gpu-information)
    + [Step 5. Run a trial simulation](#step-5-run-a-trial-simulation)
    + [Step 6. Test MATLAB for visualization](#step-6-test-matlab-for-visualization)
    + [Step 7. Setting up MATLAB search path](#step-7-setting-up-matlab-search-path)
  * [Running Simulations](#running-simulations)
  * [Using JSON-formatted input files](#using-json-formatted-input-files)
  * [Using JSON-formatted shape description files](#using-json-formatted-shape-description-files)
  * [Output data formats](#output-data-formats)
    + [Volumetric output](#volumetric-output)
    + [Detected photon data](#detected-photon-data)
    + [Photon trajectory data](#photon-trajectory-data)
  * [Using mcxlabcl in MATLAB and Octave](#using-mcxlabcl-in-matlab-and-octave)
  * [Using MCXStudio GUI](#using-mcxstudio-gui)
  * [Interpreting the Output](#interpreting-the-output)
    + [Output files](#output-files)
    + [Guide for output units](#guide-for-output-units)
    + [Console print messages](#console-print-messages)
  * [Best practices guide](#best-practices-guide)
  * [Acknowledgement](#acknowledgement)
  * [Reference](#reference)


What's New
-------------

MCX-CL v2025 is a maintenance release with multiple bug fixes and minor new features.
It is highly recommended to upgrade for all users.

Notable major bug fixes include
* a high priority bug fangq/mcx#222, introduced in 198cd34, was fixed. this bug affects all simulations
  since v2023. Particularly, when a photon has a long pathlength, with weight drops to numerical 0,
  its pathlength data is carried in the immediately launched new photon, causing skews in the
  detected photon pathlength distributions
* multi-GPU simulations was not working (serialized) in v2024.2, this has been fixed
* further updates to the handling of low absorption medium (fangq/mcx#164), previously affect not-so-low mua values
* fix incorrect angleinvcdf and invcdf buffer length, ported from fangq/mcx#233
* fix missing nscat output due to incorrect macro, fix #56
* add missing --maxjumpdebug flag, accept float, fix #54
* avoid double-base64-encoding when -Z 2 is used, fangq/mcx#219
* for all brain related simulations, we have updated the CSF mua value of 0.004/mm that was
  previously used in Custo et al. 2006 paper to 0.0004/mm, matching its upstream reference Strangman et al. 2003.
  However, we want to highlight that both literature may not provide the best mua value for CSF - as such low
  mua/mus CSF properties are mostly for CSF in the inner part of the brain, but not representiative to those in
  the subarachnoid space. A few literature have shown that CSF in the subarachnoid space may have a higher
  mus' value, in the range between 0.16/mm to 0.32/mm, as shown in Okada et al. 2003

* [Okada_2003] E. Okada and D. T. Delpy, “Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer,” Applied Optics 42, 2906–2914 (2003)
* [Custo_2006] A. Custo, W. M. Wells III., A. H. Barnett, et al., “Effective scattering coefficient of the cerebral spinal fluid in adult head models for diffuse optical imaging,” Applied Optics 45, 4747 (2006)
* [Strangman_2003] G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” NeuroImage 18, 865–879 (2003)

In addition, in this release, we also added the following key new features

* optimize thread configurations on Apple processors (M1/M2/M3/M4) and achieve 7x speed acceleration
* the new `-N/--net` command line flag allows one to browse and run growing number of community-contributed
  simulations hosted on https://neurojson.io (one can browse the list at https://neurojson.org/db/mcx)
* mcx can read stdin (standard input) using pipe, allow one to use advanced text processing utilities in the shell,
  such as `sed, perl, jq` to modify JSON inputs at runtime. For example `mcx -N cube60 | jq '.Forward.Dt=1e-10' | mcx -f`
* a new shortcut option `-Q` for `--bench` to conveniently browse and run built-in benchmarks
* mcxlabcl and pmcxcl cam set `cfg.flog=1` or 0 to disable printing of mcxcl banner


The detailed updates can be found in the below change log

* 2025-01-23 [2a9476b] [feat] support reading pipe from command line with -f -
* 2025-01-22 [febd932] [bug] fix incorrect per-voxel pathlength when mua->0, fangq/mcx#164
* 2025-01-21 [3640d79] [bug] fix windows -N error
* 2025-01-21 [44f63c0] [cmake] update cmake to add -N support
* 2025-01-20 [6de71b0] [neurojson] avoid needing c++11, fix ci
* 2025-01-20 [3cb78c4] [ci] fix mac warnings and errors
* 2025-01-20 [29b0f3b] [feat] add -N/--net to download simulations from NeuroJSON.io, add -Q
* 2024-11-13 [d79f6ed] [ci] remove mac local file
* 2024-11-13 [96b8dbc] [ci] speedup macos-13 and windows octave download
* 2024-11-13 [299b920] [ci] add install octave using dmg
* 2024-11-13 [f8bb370] [ci] add windows octave mex build back
* 2024-09-30 [0f642c3] [pmcxcl] bump pmcxcl version to 0.2.1 to include fix in fangq/mcx#233
* 2024-09-29 [42917da] [bug] fix incorrect angleinvcdf and invcdf length, fangq/mcx#233
* 2024-09-14 [f15814c] [ci] revert pybind11 version further for python3.6
* 2024-09-14 [d3baf8c] [ci] add ubuntu-24.04 and macos-14
* 2024-09-14 [e2fa087] [ci] remove DL_LDFLAGS
* 2024-09-14 [dd99f92] [ci] macos ci fails with -shared
* 2024-09-14 [7a3e4aa] [ci] test without macos-14
* 2024-09-13 [4b9e21b] [bug] fix the potential typo in Custo et al for CSF mua, fangq/mcx#232
* 2024-09-11 [c13dcf2] [ci] remove upx as github disabled upx for macos
* 2024-09-11 [2191885] [ci] allow make oct to link with OpenCL on macos
* 2024-09-03 [7c57318] [ci] fix action alert related to download-artifact https://github.com/NeuroJSON/zmat/security/dependabot/1
* 2024-08-20 [8f84acf] [ci] use gcc-12 on macos runner
* 2024-08-18 [d7defeb] [bug] fix lzma memory leakage, NeuroJSON/zmat#11, lloyd/easylzma#4
* 2024-07-19 [9c86f6b] [bug] fix multi-gpu serialization bug on nvidia GPUs the bug was introduced in https://github.com/fangq/mcxcl/commit/9b4d76f03d86c2c9f3f449e340925649d4514379
* 2024-06-12 [953c4e9] [ci] remove compiler warnings
* 2024-06-12 [57612d0] [test] make mcxcl pass all tests on Alder lake Intel iGPU
* 2024-06-12 [4609451] [bug] detector radius is not squared, fix #58
* 2024-06-11 [2b91626] [intel] pass all tests and remove all warnings on old i7-2635QM on mac
* 2024-06-11 [59d88a0] [test] sync test script between mcx and mcxcl after #57
* 2024-06-11 [88899f9] [bug] reset replay.tof when tof too big, improve replay test, fix #57
* 2024-06-09 [6e91fe3] [opencl] optimizing thread number on Apple silicon, gain 7x speedup
* 2024-06-09 [c2bbf7f] [ci] test on macos-13
* 2024-06-09 [7b558a4] [bug] fix colin27 failure on Apple silicon, fix #49
* 2024-06-08 [93c589c] [ci] trying to fix macos-12 build
* 2024-06-08 [9e84ecc] [bug] apply the same patch to ensure shared mem reset, fangq/mcx#222
* 2024-06-08 [1154916] [bug] fix missing nscat output due to incorrect macro, fix #56
* 2024-06-05 [8918150] [bug] add missing --maxjumpdebug flag, accept float, fix #54
* 2024-06-04 [4b00f6d] [bug] fix double fclose error, close #53
* 2024-04-25 [027cc55] [bug] avoid double-base64-encoding when -Z 2 is used, fangq/mcx#219
* 2024-03-29 [cbfa564] [ci] modify DL_LDFLAGS
* 2024-03-29 [ac879dc] [ci] revert LDFLAGS changes to compare difference on macos-12
* 2024-03-29 [d175187] [ci] print verbose mkoctfile log to debug macos-12
* 2024-03-29 [334c882] [package] copy LFLAGS to LDFLAGS, close #50


Introduction
-------------

Monte Carlo eXtreme (MCX) is a fast physically-accurate photon simulation 
software for 3D heterogeneous complex media. By taking advantage of 
the massively parallel threads and extremely low memory latency in a 
modern graphics processing unit (GPU), this program is able to perform Monte 
Carlo (MC) simulations at a blazing speed, typically hundreds to
a thousand times faster than a single-threaded CPU-based MC implementation.

MCX-CL is the OpenCL implementation of the MCX algorithm. Unlike MCX
which can only be executed on NVIDIA GPUs, MCX-CL is written in OpenCL,
the Open Computing Language, and can be executed on most modern CPUs
and GPUs available today, including Intel and AMD CPUs and GPUs. MCX-CL
is highly portable, highly scalable and is feature-rich just like MCX.

Due to the nature of the underlying MC algorithms, MCX and MCX-CL are
ray-tracing/ray-casting software under-the-hood. Compared to commonly
seen ray-tracing libraries used in computer graphics or gaming
engines, MCX-CL and MCX have many unique characteristics. The most
important difference is that MCX/MCX-CL are rigorously based on physical
laws. They are numerical solvers to the underlying radiative transfer equation
(RTE) and their solutions have been validated across many publications
using optical instruments and experimental measurements. In comparison,
most graphics-oriented ray-tracers have to make many approximations in
order to achieve fast rendering, enable to provide quantitatively accurate
light simulation results. Because of this, MCX/MCX-CL have been extensively
used by biophotonics research communities to obtain reference solutions and
guide the development of novel medical imaging systems or clinical
applications. Additionally, MCX/MCX-CL are volumetric ray-tracers; they
traverse photon-rays throughout complex 3-D domains and computes physically
meaningful quantities such as spatially resolved fluence, flux, diffuse
reflectance/transmittance, energy deposition, partial pathlengths,
among many others. In contrast, most graphics ray-tracing engines
only trace the RGB color of a ray and render it on a flat 2-D screen.
In other words, MCX/MCX-CL gives physically accurate 3-D light distributions
while graphics ray-tracers focus on 2-D rendering of a scene at the camera.
Nonetheless, they share many similarities, such as ray-marching computation,
GPU acceleration, scattering/absorption handling etc.

The details of MCX-CL can be found in the below paper

> [Yu2018] Leiming Yu, Fanny Nina-Paravecino, David Kaeli, and Qianqian Fang, 
"Scalable and massively parallel Monte Carlo photon transport simulations 
for heterogeneous computing platforms," J. Biomed. Optics, 23(1), 010504 (2018).

A short summary of the main features includes:

* 3D heterogeneous media represented by voxelated array
* support over a dozen source forms, including wide-field and pattern illuminations
* support a number of boundary conditions
* time-resolved photon transport simulations
* saving photon partial path lengths, trajectories and other per-photon data
* optimized random number generators
* build-in flux/fluence normalization to output Green's functions
* user-adjustable voxel resolution
* improved accuracy with atomic operations
* cross-platform graphical user interface
* native Matlab/Octave/Python interfaces for high usability
* flexible JSON interface for future extensions
* multi-GPU support
* advanced features: photon-replay, photon-sharing, and more

MCX-CL can be used on Windows, Linux and Mac OS. Multiple user 
interfaces are provided, including

- **Command line mode:** mcxcl can be executed in the command line, best suited 
 for batch data processing
- **Graphical User Interface with MCXStudio:** MCXStudio is a unified GUI program 
 for MCX, MCX-CL and MMC. One can intuitively set all parameters, including GPU 
 settings, MC settings and domain design, in the cross-platform interface
- **Calling inside MATLAB/Octave:** mcxlabcl is a mex function, one can call it 
 inside MATLAB or GNU Octave to get all functionalities as the command line version.
- **Calling inside Python:** `pmcxcl` is a Python module wrapping the entire mcxcl
 simulation in an easy-to-use interface. One can install pmcxcl via `pip install pmcxcl`

If a user is familiar with MATLAB/Octave/Python, it is highly recommended to 
use MCXCL in MATLAB/Octave/Python to ease data visualization. If one prefers a 
GUI, please use MCXStudio to start.  For users who are familiar with MCX/MCXCL 
and need it for regular data processing, using the command line mode is 
recommended.


Requirement and Installation
----------------------------

With the up-to-date driver installed for your computers, MCXCL can run on
almost all computers. The requirements for using this software include

* a modern CPU or GPU (Intel, NVIDIA, AMD, among others)
* pre-installed graphics driver - typically includes the OpenCL runtime (`libOpenCL.*` or `OpenCL.dll`)

For speed differences between different CPUs/GPUs made by different vendors, please
see your above paper [1] and our websites

- http://mcx.space/computebench/
- http://mcx.space/mcxcl

Generally speaking, AMD and NVIDIA high-end dedicated GPUs perform the best, about 20-60x 
faster than a multi-core CPU; Intel's integrated GPU is about 3-4 times faster than
a multi-core CPU.

MCX-CL supports and has been fully tested with open-source OpenCL runtime 
pocl (http://portablecl.org/) on the CPU. To install pocl, please run
```
sudo apt-get install pocl-opencl-icd
```

To install MCXCL, you simply download the binary executable corresponding to your 
computer architecture and platform, extract the package 
and run the executable under the `{mcxcl root}/bin` directory. For Linux
and MacOS users, please double check and make sure `libOpenCL.so` is installed under 
the `/usr/lib` directory. If it is installed under a different directory, please
define environment variable `LD_LIBRARY_PATH` to include the path.

If `libOpenCL.so` or `OpenCL.dll` does not exist on your system or, please
make sure you have installed [CUDA SDK](https://developer.nvidia.com/cuda-toolkit) (if you are using an NVIDIA card)
or AMD APP SDK (if you are using an AMD card), e.g. [Radeon™ Software for Linux](https://www.amd.com/en/support) or [ROCm](https://github.com/RadeonOpenCompute/ROCm) where the option for OpenCL was selected. 

Additionally, if you are on a Linux-based system you may require the below dependences - this command assumes you are running Ubuntu:
```
sudo apt-get install ocl-icd-libopencl1 opencl-headers ocl-icd-opencl-dev zlib1g-dev 
```

The below installation steps can be browsed online at 

http://mcx.space/wiki/index.cgi/wiki/index.cgi?Workshop/MCX18Preparation/MethodA


### Step 1. Verify your CPU/GPU support

MCX-CL supports a wide-range of processors, including Intel/AMD CPUs 
and GPUs from NVIDIA/AMD/Intel. If your computer has been working previously,
in most cases, MCX-CL can simply run out-of-box. However, if you have trouble,
please follow the below detailed steps to verify and setup your OS to run
MCX-CL.

#### Verify GPU/CPU support

To verify if you have installed the OpenCL or CUDA support, you may

* if you have a windows machine, download and install the 
[Everything Search](https://www.voidtools.com/) tool (a small and 
 fast file name search utility), and type `opencl.dll` in the search bar 
 **Expected result**: you must see `OpenCL.dll` (or `nvopencl.dll`
 if you have an NVIDIA GPU) installed in the `Windows\System32` directory.
* if you have a Mac, open a terminal, and type `ls /System/Library/Frameworks/OpenCL.framework/Versions/A/OpenCL`
** **Expected result**: you should not see an error.
* if you have a Linux laptop, open a terminal, and type `locate libOpenCL.so`, 
** **Expected result**: you should see one or multiple libOpenCL files

If the `OpenCL.dll` file is not found on your Windows machine, please 
read the below sections. Otherwise, please go to Step 2: Install MATLAB.

#### Computers without discrete GPUs

In many cases, your computer runs on an Intel CPU with integrated graphics. In 
this case, please make sure you have installed the latest Intel graphics drivers. 
If you are certain that you have installed the graphics drivers, or your 
graphics works smoothly, please skip this step.

If you want to double check, for Windows machine, you can download the 
"Intel Driver&Support Assistant" to check if you have installed the 
graphics drivers from https://www.intel.com/content/www/us/en/developer/articles/tool/opencl-drivers.html

For a Mac, you need to use your App store to update the driver, see the 
below link for details https://www.intel.com/content/www/us/en/support/articles/000022440/graphics-drivers.html

for a Linux (for example Ubuntu) laptop, the intel CPU OpenCL run-time 
can be downloaded from https://software.intel.com/en-us/articles/opencl-drivers#latest_CPU_runtime

if you want to use both Intel CPU and GPU on Linux, you need to install 
the OpenCL™ 2.0 GPU/CPU driver package for Linux* (this involves compiling a new kernel) 
https://software.intel.com/en-us/articles/opencl-drivers#latest_linux_driver

#### Computers with discrete GPUs

If you have a computer with a discrete GPU, you need to make sure your 
discrete GPU is configured with the appropriate GPU 
driver installed. Again, if you have been using your laptop regularly and 
the graphics has been smooth, likely your graphics driver has already been 
installed.

If your GPU driver was not installed, and would like to install, or upgrade 
from an older version, for an NVIDIA GPU, you may browse this link to 
install the matching driver from http://www.nvidia.com/Download/index.aspx

If your GPU is an AMD GPU, please use the below link https://support.amd.com/en-us/download

It is also possible to simultaneously access Intel CPU along with your 
discrete GPU. In this case, you need to download the latest Intel OpenCL 
Runtime for CPU only if you haven't installed it already from 
https://software.intel.com/en-us/articles/opencl-drivers#latest_CPU_runtime

**Note:** If you have an NVIDIA GPU, there is no need to install CUDA in 
order for you to run MCX-CL/MCXLABCL.

#### Computers with hybrid GPUs

We noticed that running Ubuntu Linux 22.04 with a 6.5 kernel on a laptop with 
a hybrid GPU with an Intel iGPU and an NVIDIA GPU, you must configure the
laptop to use the NVIDIA GPU as the primary GPU by choosing "NVIDIA (Performance Mode)"
in the PRIME Profiles section of **NVIDIA X Server Settings**. You can also run 

```
sudo prime-select nvidia
```

to achieve the same goal. Otherwise, the simulation may hang your system
after running for a few seconds. A hybrid GPU laptop combing an NVIDIA GPU 
with an AMD iGPU does not seem to have this issue if using Linux.

In addition, NVIDIA drirver (520 or newer) has a known glitch running on Linux kernel
6.x (such as those in Ubuntu 22.04). See

https://forums.developer.nvidia.com/t/dev-nvidia-uvm-io-error-on-ubuntu-22-04-520-to-535-driver-versions/262153

When the laptop is in the "performance" mode and wakes up from suspension, MCX/MCX-CL/MMC or any
CUDA program fails to run with an error


```
MCX ERROR(-999):unknown error in unit mcx_core.cu:2523
```

This is because the kernel module `nvida-uvm` fails to be reloaded after suspension.
If you had an open MATLAB session, you must close MATLAB first, and
run the below commands (if MATLAB is open, you will see `rmmod: ERROR: Module nvidia_uvm is in use`)

```
sudo rmmod /dev/nvidia-uvm
sudo modprobe nvidia-uvm
```

after the above command, MCX-CL should be able to run again.


### Step 2. Install MATLAB or GNU Octave

One must install either a MATLAB or GNU Octave if one needs to use `mcxlabcl`.
If you use a Mac or Linux laptop, you need to create a link (if this link does
not exist) so that your system can find MATLAB. To do this you start a terminal, 
and type

	sudo ln -s /path/to/matlab /usr/local/bin

please replace `/path/to/matlab` to the actual `matlab` command 
full path (for Mac, this is typically `/Application/MATLAB_R20???.app/bin/matlab`, 
for Linux, it is typically `/usr/local/MATLAB/R20???/bin/matlab`, `???` 
is the year and release, such as 18a, 17b etc). You need to type your 
password to create this link.

To verify your computer has MATLAB installed, please start a terminal on a 
Mac or Linux, or type `cmd` and enter in Windows start menu, in the terminal, 
type `matlab` and enter, you should see MATLAB starts.

### Step 3. Installing MCXCL

One can either choose to compile mcxcl locally or download pre-compiled binaries from MCX's website. 

#### Compile From Source

This section assumes you are on a Linux-based machine, e.g. Ubunutu, CentOS, etc.

Clone this repository:
```
git clone https://github.com/fangq/mcxcl.git
```

From here we can either use the latest master branch (default) or latest release, which is v2020 currently. 
To change which release you are using we have to enter the directory we just cloned:
```
cd mcxcl
```

We can start compiling from source. Now enter the `src` folder:
```
cd src
```
We can do one of three things here - compile a standalone binary of mcxcl, a MATLAB/mex version, or an Octave/oct version. 

##### Standalone Binary

Simply run: `make` 

Which will compile a binary and place it in `mcxcl/bin`

##### MATLAB

Make sure you have mex in your `$PATH`, which can be done via:
```
export PATH="/path/to/matlab/bin/mex:$PATH"
```

Run: `make mex`

This will output the mex file to `mcxcl/mcxlabcl`

##### Octave

Make sure you have the following dependences - assuming you are running Ubuntu:

```
sudo apt install -y liboctave-dev
```

Run: `make oct`

This will output the octave/mex file to `mcxcl/mcxlabcl`

##### Python

Make sure you have the following dependences - assuming you are running Ubuntu:

```
sudo apt install -y python3-dev python3-pip python3-wheel cmake
```

Run: `cd pmcxcl && python3 -m pip wheel .`

This will output a binary wheel file `pmcxcl-X.Y.Z-cpXX-*.whl` in the `pmcxcl` folder.
One can install it using

```
python3 -m pip install pmcxcl*.whl
```

##### Misc

After you have compilied and copied your binary to a seperate location you can run `make clean` to remove all the build files that were 
created during compilation. 

#### Download MCXCL

One can download two separate MCXCL packages (standalone mcxcl binary, and mcxlabcl)
or download the integrated MCXStudio package (which contains mcx, mcxcl, mmc, mcxlab, 
mcxlabcl and mmclab) where both packages, and many more, are included. The latest
stable released can be found on the MCX's website. However, if you want to use the
latest (but sometimes containing half-implemented features) software, you can access
the nightly-built packages from http://mcx.space/nightly/

If one has downloaded the mcxcl binary package, after extraction, you may open
a terminal (on Windows, type cmd the Start menu), cd mcxcl folder and then cd
the bin subfolder. Please type "mcxcl" and enter, if the binary is compatible with
your OS, you should see the printed help info. The next step is to run 

	mcxcl -L

This will query your system and find any hardware that can run mcxcl. If your
hardware (CPU and GPU) have proper driver installed, the above command will typically
return 1 or more available computing hardware. Then you can move to the next step.

If you do not see any processor printed, that means your CPU or GPU does not have
OpenCL support (because it is too old or no driver installed). You will need to 
go to their vendor's website and download the latest driver. For Intel CPUs older
than Ivy Bridge (4xxx), OpenCL and MCXCl are not well supported. Please consider
installing dedicated GPU or use a different computer.

In the case one has installed the MCXStudio package, you may follow the below 
procedure to test for hardware compatibility.

Please click on the folder matching your operating system (for example, if you run 
a 64bit Windows, you need to navigate into `win64` folder), and download 
the file named  `"MCXStudio-nightlybuild-installer.exe"`.

Open this file, and unzip it to a working folder (for Windows, for example, the 
`Documents` or `Downloads` folder). The package needs about 100 MB disk space.

Once unzipped, you should be able to see a folder named `MCXStudio`, 
with a few executables and 3 subfolders underneath. See the folder structure below:

<pre>MCXStudio/
├── MATLAB/
│   ├── mcxlab/
│   ├── mcxlabcl/
│   └── mmclab/
├── MCXSuite/
│   ├── mcx/
│   ├── mcxcl/
│   └── mmc/
├── mcxstudio
├── mcxshow
└── mcxviewer
</pre>

Please make sure that your downloaded `MCXStudio` must match your operating system.

#### Notes for Mac Users
**For Mac users:** Please unzip the package under your 
[home directory](https://www.cnet.com/how-to/how-to-find-your-macs-home-folder-and-add-it-to-finder/) directly (Shift+Command+H).

#### Notes for Windows Users
When you start MCXStudio, you may see a dialog to ask you to modify the `TdrDelay` key 
in the registry so that mcx can run more than 5 seconds. If you select `Yes`, some 
of you may get an error saying you do not have permission. 

To solve this problem, you need to quit MCXStudio, and then right-click on the 
`mcxstudio.exe`, and select "Run as Administrator". Then, you should be 
able to apply the registry change successfully. 

Alternatively, one should open file browser, navigate into `mcxcl/setup/win64` folder,
and right-click on the `apply_timeout_registry_fix.bat` file and select 
"Run as Administrator".

	You must reboot your computer for this change to be effective!

### Step 4. Start MCXStudio and query GPU information

Now, navigate to the MCXStudio folder (i.e. the top folder of the extracted 
software structure). On Windows, right-click on the executable named `"mcxstudio.exe"` 
and select "Run as Administrator" for the first time only; on the Linux, 
double click on the `mcxstudio` executable; on the Mac, open a 
terminal and type

	cd ~/MCXStudio
	open mcxstudio.app

First, click on the "New" button to the top-left (green plus icon), 
select the 3rd option "`NVIDIA/AMD/Intel CPUs/GPUs (MCX-CL)`", 
and type a session name **"test"** in the field below. Then click 
OK. You should see a blue/yellow "test" icon added to the left panel. 

Now, click on the **"GPU"** button on the toolbar (6th button from 
the left side), an Output window will popup, and wait for a few seconds, 
you should see an output like

<pre>"-- Run Command --"
"mcxcl" -L
"-- Printing GPU Information --"
Platform [0] Name NVIDIA CUDA
============ GPU device ID 0 [1 of 2]: Graphics Device  ============
 Device 1 of 2:		Graphics Device
 Compute units   :	80 core(s)
 Global memory   :	12644188160 B
 Local memory    :	49152 B
 Constant memory :	65536 B
 Clock speed     :	1455 MHz
 Compute Capacity:	7.0
 Stream Processor:	10240
 Vendor name    :	NVIDIA
 Auto-thread    :	655360
...
Platform [1] Name Intel(R) OpenCL
============ CPU device ID 2 [1 of 1]: Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz  ============
 Device 3 of 1:		Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz
 Compute units   :	8 core(s)
 Global memory   :	33404575744 B
 Local memory    :	32768 B
 Constant memory :	131072 B
 Clock speed     :	4200 MHz
 Vendor name    :	Intel
 Auto-thread    :	512
 Auto-block     :	64

"-- Task completed --"
</pre>

Your output may look different from above. If you do not see any output, or 
it returns no GPU found, that means your OpenCL support was not installed 
properly. Please go back to Steps 1-2 and reinstall the drivers. 

If you have Intel CPU with Integrated GPU, you should be able to see a section with 
**"Platform [?] Name Intel(R) OpenCL"** in the above output. You may see only 
the CPU is listed, or both the CPU and the integrated GPU.


### Step 5. Run a trial simulation

If your above GPU query was successful, you should now see in the middle panel 
of the MCXStudio window, under the Section entitled "GPU Settings", in a check-box 
list under "Run MCX on", you should now see the available devices on your laptop. 

To avoid running lengthy simulations, please change the _"Total photon number (-n)"_ 
field under the _"Basic Settings"_ from `1e6` to `1e5`.

Now, you can then run a trial simulation, by first clicking on the "Validate" 
button (blue check-mark icon), and then click on "Run" (the button to the 
right of validate). This will launch an MCXCL simulation. The output window 
will show again, and you can see the messages printed from the simulation, 
similar to the output below:

<pre>"-- Command: --"
mcxcl --session "preptest"  --input "/Output/mcxclsessions/preptest/preptest.json" --root "/Output/mcxclsessions/preptest" --outputformat mc2 --gpu 10 --autopilot 1 --photon 10000000 --normalize 1 --save2pt 1 --reflect 1 --savedet 1 --unitinmm 1.00 --saveseed 0 --seed "1648335518" --compileropt "-D USE_ATOMIC" --array 0 --dumpmask 0 --repeat 1  --maxdetphoton 10000000
"-- Executing Simulation --"
==============================================================================
=                       Monte Carlo eXtreme (MCX) -- OpenCL                  =
=          Copyright (c) 2010-2025 Qianqian Fang <q.fang at neu.edu>         =
=                https://mcx.space/  &  https://neurojson.io                 =
=                                                                            =
= Computational Optics&Translational Imaging (COTI) Lab - http://fanglab.org =
=   Department of Bioengineering, Northeastern University, Boston, MA, USA   =
==============================================================================
=    The MCX Project is funded by the NIH/NIGMS under grant R01-GM114365     =
==============================================================================
= Open-source codes and reusable scientific data are essential for research, =
= MCX proudly developed human-readable JSON-based data formats for easy reuse=
=                                                                            =
=Please visit our free scientific data sharing portal at https://neurojson.io=
= and consider sharing your public datasets in standardized JSON/JData format=
==============================================================================
$Rev::4fdc45 $ v2025 $Date::2018-03-29 00:35:53 -04$by $Author::Qianqian Fang$
==============================================================================
- variant name: [Detective MCXCL] compiled with OpenCL version [1]
- compiled with: [RNG] Logistic-Lattice [Seed Length] 5
initializing streams ...	init complete : 0 ms

Building kernel with option: -cl-mad-enable -DMCX_USE_NATIVE -DMCX_SIMPLIFY_BRANCH -DMCX_VECTOR_INDEX -DMCX_SRC_PENCIL  -D USE_ATOMIC -DUSE_ATOMIC -D MCX_SAVE_DETECTORS -D MCX_DO_REFLECTION
build program complete : 23 ms
- [device 0(1): Graphics Device] threadph=15 oddphotons=169600 np=10000000.0 nthread=655360 nblock=64 repetition=1
set kernel arguments complete : 23 ms
lauching mcx_main_loop for time window [0.0ns 5.0ns] ...
simulation run# 1 ... 

kernel complete:  	796 ms
retrieving flux ... 	
detected 0 photons, total: 0	transfer complete:        818 ms
normalizing raw data ...	normalization factor alpha=20.000000
saving data to file ... 216000 1	saving data complete : 821 ms

simulated 10000000 photons (10000000) with 1 devices (repeat x1)
MCX simulation speed: 12953.37 photon/ms
total simulated energy: 10000000.00	absorbed: 27.22654%
(loss due to initial specular reflection is excluded in the total)
</pre>

If this simulation is completed successfully, you should be able to see the 
"Simulation speed" and total simulated energy reported at the end. Please 
verify your "absorbed" percentage value printed at the end (in bold above), 
and make sure it is **~27%**. We found that some Intel OpenCL runtime
versions produced incorrect results. 

If your laptop shows an error for the Intel GPU, please choose another 
device from the "GPU Settings" section, and run the simulation again. 

If your GPU/CPU gives the below error (found on HD4400 GPU and 4th gen Intel CPUs)

	error: OpenCL extension 'cl_khr_fp64' is unsupported
	MCXCL ERROR(11):Error: Failed to build program executable! in unit mcx_host.cpp:510

You may add 

	-J "-DUSE_LL5_RAND"

in the `MCXStudio GUI`>`Advanced Settings`>`Additional Parameters` 
field. This should allow it to run, but please verify the absorption fraction 
is ~27%. For 4th generation Intel CPU, we found that install the Intel CPU 
OpenCL run-time can produce correct simulations. Please download it from 
https://software.intel.com/en-us/articles/opencl-drivers#latest_CPU_runtime

### Step 6. Test MATLAB for visualization

From v2019.3, MCXStudio provides builtin 3D volume visualization, this step is no longer needed.

### Step 7. Setting up MATLAB search path

The next step is to set up the search paths for MCXLAB/MMCLAB. You need to 
start MATLAB, and in the Command window, please type 

	pathtool

this will popup a window. Click on the "Add with Subfolders ..." button 
(the 2nd from the top), then browse the MCXStudio folder, then select 
OK. Now you should see all needed MCX/MMC paths are added to MATLAB. 
Before you quick this window, click on the "Save" button.

To verify if your MCXLAB/MMCLAB/MCXLABCL has been installed properly, please type

	which mcxlab
	which mmclab
	which mcxlabcl

you should see their full paths printed. 


To see if you can run MCXLAB-CL in your environment, please type 

	USE_MCXCL=1   ''%define this line in the base workspace, all subsequent mcxlab calls will use mcxcl''
	info=mcxlab('gpuinfo')
	clear USE_MCXCL

this should print a list of CPU/GPU devices using which you can run the MC simulations.

	upload: matlab_gpu_verify.png

If you do not see any output, that means your CPU/GPU OpenCL driver was not installed 
properly, you need to go back to Steps 1-2.

If you have an NVIDIA GPU, and have installed the proper GPU driver, you may run

	info=mcxlab('gpuinfo')  % notice the command is mcxlab instead of mcxlabcl

this should print a list of NVIDIA GPU from the MATLAB window.


Running Simulations
----------------------------

To run a simulation, the minimum input is a configuration (text) file,
and a volume (a binary file with each byte representing a medium 
index). Typing the name of the executable without any parameters, 
will print the help information and a list of supported parameters, 
such as the following:

<pre>==============================================================================
=                       Monte Carlo eXtreme (MCX) -- OpenCL                  =
=          Copyright (c) 2010-2025 Qianqian Fang <q.fang at neu.edu>         =
=                https://mcx.space/  &  https://neurojson.io                 =
=                                                                            =
= Computational Optics&Translational Imaging (COTI) Lab - http://fanglab.org =
=   Department of Bioengineering, Northeastern University, Boston, MA, USA   =
==============================================================================
=    The MCX Project is funded by the NIH/NIGMS under grant R01-GM114365     =
==============================================================================
= Open-source codes and reusable scientific data are essential for research, =
= MCX proudly developed human-readable JSON-based data formats for easy reuse=
=                                                                            =
=Please visit our free scientific data sharing portal at https://neurojson.io=
= and consider sharing your public datasets in standardized JSON/JData format=
==============================================================================
$Rev::4fdc45 $ v2025 $Date::2018-03-29 00:35:53 -04$by $Author::Qianqian Fang$
==============================================================================

usage: mcxcl <param1> <param2> ...
where possible parameters include (the first value in [*|*] is the default)

== Required option ==
 -f config.json  (--input)     read an input file in the .json format,if config
                               string starts with '{',it is parsed as an inline
                               JSON input file; if -f is followed by nothing or
                               a single '-', it reads input from stdin via pipe
      or
 -Q/--bench [cube60, skinvessel,...] run a buint-in benchmark specified by name
                               run -Q without parameter to get a list
 -N benchmark  (--net)         get benchmark from NeuroJSON.io, -N only to list
                               benchmark can be dataset URL,or dbname/benchname
                               requires 'curl', install from https://curl.se/

== MC options ==
 -n [0|int]    (--photon)      total photon number (exponential form accepted)
 -r [1|+/-int] (--repeat)      if positive, repeat by r times,total= #photon*r
                               if negative, divide #photon into r subsets
 -b [1|0]      (--reflect)     1 to reflect photons at ext. boundary;0 to exit
 -B '______'   (--bc)          per-face boundary condition (BC), 6 letters for
    /case insensitive/         bounding box faces at -x,-y,-z,+x,+y,+z axes;
                               overwrite -b if given. 
                               each letter can be one of the following:
                               '_': undefined, fallback to -b
                               'r': like -b 1, Fresnel reflection BC
                               'a': like -b 0, total absorption BC
                               'm': mirror or total reflection BC
                               'c': cyclic BC, enter from opposite face

                               if input contains additional 6 letters,
                               the 7th-12th letters can be:
                               '0': do not use this face to detect photon, or
                               '1': use this face for photon detection (-d 1)
                               the order of the faces for letters 7-12 is 
                               the same as the first 6 letters
                               eg: --bc ______010 saves photons exiting at y=0
 -u [1.|float] (--unitinmm)    defines the length unit for the grid edge
 -U [1|0]      (--normalize)   1 to normalize flux to unitary; 0 save raw
 -E [0|int|.jdat] (--seed)     set random-number-generator seed, -1 to generate
                               if a jdat/mch file is followed, MCX "replays" 
                               the detected photon; the replay mode can be used
                               to calculate the mua/mus Jacobian matrices
 -z [0|1]      (--srcfrom0)    1 volume origin is [0 0 0]; 0: origin at [1 1 1]
 -Y [0|int]    (--replaydet)   replay only the detected photons from a given 
                               detector (det ID starts from 1), used with -E 
                               if 0, replay all detectors and sum all Jacobians
                               if -1, replay all detectors and save separately
 -V [0|1]      (--specular)    1 source located in the background,0 inside mesh
 -e [0.|float] (--minenergy)   minimum energy level to trigger Russian roulette
 -g [1|int]    (--gategroup)   number of maximum time gates per run

== GPU options ==
 -L            (--listgpu)     print GPU information only
 -t [16384|int](--thread)      total thread number
 -T [64|int]   (--blocksize)   thread number per block
 -A [1|int]    (--autopilot)   auto thread config:1 enable;0 disable
 -G [0|int]    (--gpu)         specify which GPU to use, list GPU by -L; 0 auto
      or
 -G '1101'     (--gpu)         using multiple devices (1 enable, 0 disable)
 -W '50,30,20' (--workload)    workload for active devices; normalized by sum
 -I            (--printgpu)    print GPU information and run program
 -o [1|int]    (--optlevel)    optimization level 0-no opt;1-4 more optimized
 -J '-DMACRO'  (--compileropt) specify additional JIT compiler options
                               A few built-in preprocessors include
              -DMCX_GPU_DEBUG  - print step-by-step debug info
 -k my_simu.cl (--kernel)      user specified OpenCL kernel source file

== Input options ==
 -P '{...}'    (--shapes)      a JSON string for additional shapes in the grid.
                               only the root object named 'Shapes' is parsed 
                               and added to the existing domain defined via -f 
                               or --bench
 -j '{...}'    (--json)        a JSON string for modifying all input settings.
                               this input can be used to modify all existing 
                   settings defined by -f or --bench
 -K [1|int|str](--mediabyte)   volume data format, use either a number or a str
       voxel binary data layouts are shown in {...}, where [] for byte,[i:]
       for 4-byte integer, [s:] for 2-byte short, [h:] for 2-byte half float,
       [f:] for 4-byte float; on Little-Endian systems, least-sig. bit on left
                               1 or byte: 0-128 tissue labels
                               2 or short: 0-65535 (max to 4000) tissue labels
                               4 or integer: integer tissue labels 
                              98 or mixlabel: label1+label2+label1_percentage
                                {[label1][label2][s:0-32767 label1 percentage]}
                              99 or labelplus: 32bit composite voxel format
                                {[h:mua/mus/g/n][s:(B15-16:0/1/2/3)(label)]}
                             100 or muamus_float: 2x 32bit floats for mua/mus
                                {[f:mua][f:mus]}; g/n from medium type 1
                             101 or mua_float: 1 float per voxel for mua
                                {[f:mua]}; mus/g/n from medium type 1
                             102 or muamus_half: 2x 16bit float for mua/mus
                                {[h:mua][h:mus]}; g/n from medium type 1
                             103 or asgn_byte: 4x byte gray-levels for mua/s/g/n
                                {[mua][mus][g][n]}; 0-255 mixing prop types 1&2
                             104 or muamus_short: 2x short gray-levels for mua/s
                                {[s:mua][s:mus]}; 0-65535 mixing prop types 1&2
       when formats 99 or 102 is used, the mua/mus values in the input volume
       binary data must be pre-scaled by voxel size (unitinmm) if it is not 1.
       pre-scaling is not needed when using these 2 formats in mcxlab/pmcx
 -a [0|1]      (--array)       1 for C array (row-major); 0 for Matlab array

== Output options ==
 -s sessionid  (--session)     a string to label all output file names
 -O [X|XFEJPML](--outputtype)  X - output flux, F - fluence, E - energy density
                               J - Jacobian (replay mode),   P - scattering
                               event counts at each voxel (replay mode only)
                               M - momentum transfer; L - total pathlength
 -d [1|0-3]    (--savedet)     1 to save photon info at detectors; 0 not save
                               2 reserved, 3 terminate simulation when detected
                               photon buffer is filled
 -w [DP|DSPMXVW](--savedetflag)a string controlling detected photon data fields
    /case insensitive/         1 D  output detector ID (1)
                               2 S  output partial scat. even counts (#media)
                               4 P  output partial path-lengths (#media)
                               8 M  output momentum transfer (#media)
                              16 X  output exit position (3)
                              32 V  output exit direction (3)
                              64 W  output initial weight (1)
      combine multiple items by using a string, or add selected numbers together
      by default, mcx only saves detector ID and partial-path data
 -x [0|1]      (--saveexit)    1 to save photon exit positions and directions
                               setting -x to 1 also implies setting '-d' to 1
                               same as adding 'XV' to -w.
 -X [0|1]      (--saveref)     1 to save diffuse reflectance at the air-voxels
                               right outside of the domain; if non-zero voxels
                               appear at the boundary, pad 0s before using -X
 -m [0|1]      (--momentum)    1 to save photon momentum transfer,0 not to save.
                               same as adding 'M' to the -w flag
 -q [0|1]      (--saveseed)    1 to save photon RNG seed for replay; 0 not save
 -M [0|1]      (--dumpmask)    1 to dump detector volume masks; 0 do not save
 -H [1000000] (--maxdetphoton) max number of detected photons
 -S [1|0]      (--save2pt)     1 to save the flux field; 0 do not save
 -F [jnii|...](--outputformat) fluence data output format:
                               mc2 - MCX mc2 format (binary 32bit float)
                               jnii - JNIfTI format (https://neurojson.org)
                               bnii - Binary JNIfTI (https://neurojson.org)
                               nii - NIfTI format
                               hdr - Analyze 7.5 hdr/img format
                               tx3 - GL texture data for rendering (GL_RGBA32F)
    the bnii/jnii formats support compression (-Z) and generate small files
    load jnii (JSON) and bnii (UBJSON) files using below lightweight libs:
      MATLAB/Octave: JNIfTI toolbox   https://github.com/NeuroJSON/jnifti, 
      MATLAB/Octave: JSONLab toolbox  https://github.com/NeuroJSON/jsonlab,
      Python:        PyJData:         https://pypi.org/project/jdata
      JavaScript:    JSData:          https://github.com/NeuroJSON/jsdata
 -Z [zlib|...] (--zip)         set compression method if -F jnii or --dumpjson
                               is used (when saving data to JSON/JNIfTI format)
                               0 zlib: zip format (moderate compression,fast) 
                               1 gzip: gzip format (compatible with *.gz)
                               2 base64: base64 encoding with no compression
                               3 lzip: lzip format (high compression,very slow)
                               4 lzma: lzma format (high compression,very slow)
                               5 lz4: LZ4 format (low compression,extrem. fast)
                               6 lz4hc: LZ4HC format (moderate compression,fast)
 --dumpjson [-,0,1,'file.json']  export all settings,including volume data using
                               JSON/JData (https://neurojson.org) format for
                               easy sharing; can be reused using -f
                               if followed by nothing or '-', mcx will print
                               the JSON to the console; write to a file if file
                               name is specified; by default, prints settings
                               after pre-processing; '--dumpjson 2' prints 
                               raw inputs before pre-processing

== User IO options ==
 -h            (--help)        print this message
 -v            (--version)     print MCX revision number
 -l            (--log)         print messages to a log file instead
 -i            (--interactive) interactive mode

== Debug options ==
 -D [0|int]    (--debug)       print debug information (you can use an integer
  or                           or a string by combining the following flags)
 -D [''|RMPT]                  1 R  debug RNG
    /case insensitive/         2 M  store photon trajectory info
                               4 P  print progress bar
                               8 T  save trajectory data only, disable flux/detp
      combine multiple items by using a string, or add selected numbers together

== Additional options ==
 --atomic       [1|0]          1: use atomic operations; 0: do not use atomics
 --voidtime     [1|0]          when src is outside, 1 enables timer inside void
 --showkernel   [1|0]          1:display the default or loaded (-k) MCXCL kernel
 --root         [''|string]    full path to the folder storing the input files
 --internalsrc  [0|1]          set to 1 to skip entry search to speedup launch
 --gscatter     [1e9|int]      after a photon completes the specified number of
                               scattering events, mcx then ignores anisotropy g
                               and only performs isotropic scattering for speed
 --maxvoidstep  [1000|int]     maximum distance (in voxel unit) of a photon that
                               can travel before entering the domain, if 
                               launched outside (i.e. a widefield source)
 --maxjumpdebug [10000000|int] when trajectory is requested (i.e. -D M),
                               use this parameter to set the maximum positions
                               stored (default: 1e7)

== Example ==
example: (list built-in benchmarks: -Q/--bench)
       mcxcl -Q
or (list supported GPUs on the system: -L/--listgpu)
       mcxcl -L
or (use multiple devices - 1st,2nd and 4th GPUs - together with equal load)
       mcxcl -Q cube60b -n 1e7 -G 1101 -W 10,10,10
or (use inline domain definition)
       mcxcl -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
or (use inline json setting modifier)
       mcxcl -f input.json -j '{"Optode":{"Source":{"Type":"isotropic"}}}'
or (dump simulation in a single json file)
       mcxcl -Q cube60planar --dumpjson
or (use -N/--net to browse community-contributed mcxcl simulations at https://neurojson.io)
       mcxcl -N
or (run user-shared mcxcl simulations, see full list at https://neurojson.org/db/mcx)
       mcxcl -N aircube60
or (use -f - to read piped input file modified by shell text processing utilities)
       mcxcl -Q cube60 --dumpjson | sed -e 's/pencil/cone/g' | mcxcl -f -
or (download/modify simulations from NeuroJSON.io and run with mcxcl -f)
       curl -s -X GET https://neurojson.io:7777/mcx/aircube60 | jq '.Forward.Dt = 1e-9' | mcxcl -f
</pre>

To further illustrate the command line options, below one can find a sample command
```
mcxcl -A 0 -t 16384 -T 64 -n 1e7 -G 1 -f input.json -r 2 -s test -g 10 -d 1 -w dpx -b 1
```
the command above asks mcxcl to manually (`-A 0`) set GPU threads, and launch 16384 
GPU threads (`-t`) with every 64 threads a block (`-T`); a total of 1e7 photons (`-n`)
are simulated by the first GPU (`-G 1`) and repeat twice (`-r`) - i.e. total 2e7 photons;
the media/source configuration will be read from a JSON file named `input.json` 
(`-f`) and the output will be labeled with the session id “test” (`-s`); the 
simulation will run 10 concurrent time gates (`-g`) if the GPU memory can not 
simulate all desired time gates at once. Photons passing through the defined 
detector positions are saved for later rescaling (`-d`); refractive index 
mismatch is considered at media boundaries (`-b`).

Historically, MCXCL supports an extended version of the input file format (.inp)
used by tMCimg. However, we are phasing out the .inp support and strongly 
encourage users to adopt JSON formatted (.json) input files. Many of the 
advanced MCX options are only supported in the JSON input format.

A legacy .inp MCXCL input file looks like this:

```
1000000              # total photon, use -n to overwrite in the command line
29012392             # RNG seed, negative to generate, use -E to overwrite
30.0 30.0 0.0 1      # source position (in grid unit), the last num (optional) sets --srcfrom0 (-z)
0 0 1 0              # initial directional vector, 4th number is the focal-length, 0 for collimated beam, nan for isotropic
0.e+00 1.e-09 1.e-10 # time-gates(s): start, end, step
semi60x60x60.bin     # volume ('unsigned char' binary format, or specified by -K/--mediabyte)
1 60 1 60            # x voxel size in mm (isotropic only), dim, start/end indices
1 60 1 60            # y voxel size, must be same as x, dim, start/end indices 
1 60 1 60            # y voxel size, must be same as x, dim, start/end indices
1                    # num of media
1.010101 0.01 0.005 1.37  # scat. mus (1/mm), g, mua (1/mm), n
4       1.0          # detector number and default radius (in grid unit)
30.0  20.0  0.0  2.0 # detector 1 position (real numbers in grid unit) and individual radius (optional)
30.0  40.0  0.0      # ..., if individual radius is ignored, MCX will use the default radius
20.0  30.0  0.0      #
40.0  30.0  0.0      # 
pencil               # source type (optional)
0 0 0 0              # parameters (4 floats) for the selected source
0 0 0 0              # additional source parameters
```

Note that the scattering coefficient mus=musp/(1-g).

The volume file (`semi60x60x60.bin` in the above example), can be read in two 
ways by MCX: row-major[3] or column-major depending on the value of the user 
parameter `-a`. If the volume file was saved using matlab or fortran, the 
byte order is column-major, and you should use `-a 0` or leave it out of 
the command line. If it was saved using the `fwrite()` in C, the order is 
row-major, and you can either use `-a 1`.

You may replace the binary volume file by a JSON-formatted shape file. Please 
refer to Section V for details.

The time gate parameter is specified by three numbers: start time, end time and 
time step size (in seconds). In the above example, the configuration specifies 
a total time window of [0 1] ns, with a 0.1 ns resolution. That means the 
total number of time gates is 10.

MCX provides an advanced option, -g, to run simulations when the GPU memory is 
limited. It specifies how many time gates to simulate concurrently. Users may 
want to limit that number to less than the total number specified in the input 
file - and by default it runs one gate at a time in a single simulation. But if 
there's enough memory based on the memory requirement in Section II, you can 
simulate all 10 time gates (from the above example) concurrently by using 
`-g 10` in which case you have to make sure the video card has at least 
60\*60\*60\*10\*5=10MB of free memory. If you do not include the `-g`, MCX will 
assume you want to simulate just 1 time gate at a time.. If you specify a 
time-gate number greater than the total number in the input file, (e.g, 
`-g 20`) MCX will stop when the 10 time-gates are completed. If you use the 
autopilot mode (`-A`), then the time-gates are automatically estimated for you.


Using JSON-formatted input files
-----------------------------------

Starting from version 0.7.9, MCX accepts a JSON-formatted input file in 
addition to the conventional tMCimg-like input format. JSON (JavaScript Object 
Notation) is a portable, human-readable and “fat-free” text format to 
represent complex and hierarchical data. Using the JSON format makes a input 
file self-explanatory, extensible and easy-to-interface with other applications 
(like MATLAB).

A sample JSON input file can be found under the examples/quicktest folder. The 
same file, `qtest.json`, is also shown below:
```
{
    "Help": {
      "[en]": {
        "Domain::VolumeFile": "file full path to the volume description file, can be a binary or JSON file",
        "Domain::Dim": "dimension of the data array stored in the volume file",
        "Domain::OriginType": "similar to --srcfrom0, 1 if the origin is [0 0 0], 0 if it is [1.0,1.0,1.0]",
	"Domain::LengthUnit": "define the voxel length in mm, similar to --unitinmm",
        "Domain::Media": "the first medium is always assigned to voxels with a value of 0 or outside of
                         the volume, the second row is for medium type 1, and so on. mua and mus must 
                         be in 1/mm unit",
        "Session::Photons": "if -n is not specified in the command line, this defines the total photon number",
        "Session::ID": "if -s is not specified in the command line, this defines the output file name stub",
        "Forward::T0": "the start time of the simulation, in seconds",
        "Forward::T1": "the end time of the simulation, in seconds",
        "Forward::Dt": "the width of each time window, in seconds",
        "Optode::Source::Pos": "the grid position of the source, can be non-integers, in grid unit",
        "Optode::Detector::Pos": "the grid position of a detector, can be non-integers, in grid unit",
        "Optode::Source::Dir": "the unitary directional vector of the photon at launch",
        "Optode::Source::Type": "source types, must be one of the following: 
                   pencil,isotropic,cone,gaussian,planar,pattern,fourier,arcsine,disk,fourierx,fourierx2d,
		   zgaussian,line,slit,pencilarray,pattern3d",
        "Optode::Source::Param1": "source parameters, 4 floating-point numbers",
        "Optode::Source::Param2": "additional source parameters, 4 floating-point numbers"
      }
    },
    "Domain": {
	"VolumeFile": "semi60x60x60.bin",
        "Dim":    [60,60,60],
        "OriginType": 1,
	"LengthUnit": 1,
        "Media": [
             {"mua": 0.00, "mus": 0.0, "g": 1.00, "n": 1.0},
             {"mua": 0.005,"mus": 1.0, "g": 0.01, "n": 1.0}
        ]
    },
    "Session": {
	"Photons":  1000000,
	"RNGSeed":  29012392,
	"ID":       "qtest"
    },
    "Forward": {
	"T0": 0.0e+00,
	"T1": 5.0e-09,
	"Dt": 5.0e-09
    },
    "Optode": {
	"Source": {
	    "Pos": [29.0, 29.0, 0.0],
	    "Dir": [0.0, 0.0, 1.0],
	    "Type": "pencil",
	    "Param1": [0.0, 0.0, 0.0, 0.0],
	    "Param2": [0.0, 0.0, 0.0, 0.0]
	},
	"Detector": [
	    {
		"Pos": [29.0,  19.0,  0.0],
		"R": 1.0
	    },
            {
                "Pos": [29.0,  39.0,  0.0],
                "R": 1.0
            },
            {
                "Pos": [19.0,  29.0,  0.0],
                "R": 1.0
            },
            {
                "Pos": [39.0,  29.0,  0.0],
                "R": 1.0
            }
	]
    }
}
```

A JSON input file requiers several root objects, namely `Domain`, 
`Session`, `Forward` and `Optode`. Other root sections, like 
`Help`, will be ignored. Each object is a data structure providing 
information indicated by its name. Each object can contain various sub-fields. 
The orders of the fields in the same level are flexible. For each field, you 
can always find the equivalent fields in the `*.inp` input files. For example, 
The `VolumeFile` field under the `Domain` object is the same as Line\#6 
in `qtest.inp`; the `RNGSeed` under `Session` is the same as Line\#2; the 
`Optode.Source.Pos` is the same as the triplet in Line\#3; the 
`Forward.T0` is the same as the first number in Line\#5, etc.

An MCX JSON input file must be a valid JSON text file. You can validate your 
input file by running a JSON validator, for example <http://jsonlint.com/> You 
should always use "" to quote a “name” and separate parallel items by 
“,”.

MCX accepts an alternative form of JSON input, but using it is not recommended. 
In the alternative format, you can use “`rootobj_name.field_name`”`: value` 
to represent any parameter directly in the root level. For example

    {
        "Domain.VolumeFile": "semi60x60x60.json",
        "Session.Photons": 10000000,
        ...
    }

You can even mix the alternative format with the standard format. If any input 
parameter has values in both formats in a single input file, the 
standard-formatted value has higher priority.

To invoke the JSON-formatted input file in your simulations, you can use the 
`-f` command line option with MCX, just like using an `.inp` file. For 
example:

      mcx -A 1 -n 20 -f onecube.json -s onecubejson

The input file must have a `.json` suffix in order for MCX to recognize. If 
the input information is set in both command line, and input file, the command 
line value has higher priority (this is the same for `.inp` input files). For 
example, when using `-n 20`, the value set in `Session`/`Photons`
is overwritten to 20; when using `-s onecubejson`, the 
`Session`/`ID` value is modified. If your JSON input file is invalid, 
MCX will quit and point out where the format is incorrect.


Using JSON-formatted shape description files
-----------------------------------------------

Starting from v0.7.9, MCX can also use a shape description file in the place of 
the volume file. Using a shape-description file can save you from making a 
binary `.bin` volume. A shape file uses more descriptive syntax and can be easily 
understood and shared with others.

Samples on how to use the shape files are included under the example/shapetest 
folder.

The sample shape file, `shapes.json`, is shown below:
```
{
  "MCX_Shape_Command_Help":{
     "Shapes::Common Rules": "Shapes is an array object. The Tag field sets the voxel value for each
         region; if Tag is missing, use 0. Tag must be smaller than the maximum media number in the
         input file.Most parameters are in floating-point (FP). If a parameter is a coordinate, it
         assumes the origin is defined at the lowest corner of the first voxel, unless user overwrite
         with an Origin object. The default origin of all shapes is initialized by user's --srcfrom0
         setting: if srcfrom0=1, the lowest corner of the 1st voxel is [0,0,0]; otherwise, it is [1,1,1]",
     "Shapes::Name": "Just for documentation purposes, not parsed in MCX",
     "Shapes::Origin": "A floating-point (FP) triplet, set coordinate origin for the subsequent objects",
     "Shapes::Grid": "Recreate the background grid with the given dimension (Size) and fill-value (Tag)",
     "Shapes::Sphere": "A 3D sphere, centered at C0 with radius R, both have FP values",
     "Shapes::Box": "A 3D box, with lower corner O and edge length Size, both have FP values",
     "Shapes::SubGrid": "A sub-section of the grid, integer O- and Size-triplet, inclusive of both ends",
     "Shapes::XLayers/YLayers/ZLayers": "Layered structures, defined by an array of integer triples:
          [start,end,tag]. Ends are inclusive in MATLAB array indices. XLayers are perpendicular to x-axis, and so on",
     "Shapes::XSlabs/YSlabs/ZSlabs": "Slab structures, consisted of a list of FP pairs [start,end]
          both ends are inclusive in MATLAB array indices, all XSlabs are perpendicular to x-axis, and so on",
     "Shapes::Cylinder": "A finite cylinder, defined by the two ends, C0 and C1, along the axis and a radius R",
     "Shapes::UpperSpace": "A semi-space defined by inequality A*x+B*y+C*z>D, Coef is required, but not Equ"
  },
  "Shapes": [
     {"Name":     "Test"},
     {"Origin":   [0,0,0]},
     {"Grid":     {"Tag":1, "Size":[40,60,50]}},
     {"Sphere":   {"Tag":2, "O":[30,30,30],"R":20}},
     {"Box":      {"Tag":0, "O":[10,10,10],"Size":[10,10,10]}},
     {"Subgrid":  {"Tag":1, "O":[13,13,13],"Size":[5,5,5]}},
     {"UpperSpace":{"Tag":3,"Coef":[1,-1,0,0],"Equ":"A*x+B*y+C*z>D"}},
     {"XSlabs":   {"Tag":4, "Bound":[[5,15],[35,40]]}},
     {"Cylinder": {"Tag":2, "C0": [0.0,0.0,0.0], "C1": [15.0,8.0,10.0], "R": 4.0}},
     {"ZLayers":  [[1,10,1],[11,30,2],[31,50,3]]}
  ]
 }
```
A shape file must contain a `Shapes` object in the root level. Other 
root-level fields are ignored. The `Shapes` object is a JSON array, with 
each element representing a 3D object or setting. The object-class commands 
include `Grid`, `Sphere`, `Box` etc. Each of these object include a 
number of sub-fields to specify the parameters of the object. For example, the 
`Sphere` object has 3 subfields, `O`, `R` and `Tag`. Field 
`O` has a value of 1x3 array, representing the center of the sphere; 
`R` is a scalar for the radius; `Tag` is the voxel values. The most 
useful command is `[XYZ]Layers`. It contains a series of integer 
triplets, specifying the starting index, ending index and voxel value of a 
layered structure. If multiple objects are included, the subsequent objects 
always overwrite the overlapping regions covered by the previous objects.

There are a few ways for you to use shape description records in your MCX 
simulations. You can save it to a JSON shape file, and put the file name in 
Line\#6 of your `.inp` file, or set as the value for Domain.VolumeFile field in a 
`.json` input file. In these cases, a shape file must have a suffix of `.json`.

You can also merge the Shapes section with a `.json` input file by simply 
appending the Shapes section to the root-level object. You can find an example, 
`jsonshape_allinone.json`, under examples/shapetest. In this case, you no longer 
need to define the `VolumeFile` field in the input.

Another way to use Shapes is to specify it using the `-P` (or `--shapes`) command 
line flag. For example:
```
     mcx -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
```
This will first initialize a volume based on the settings in the input `.json` 
file, and then rasterize new objects to the domain and overwrite regions that 
are overlapping.

For both JSON-formatted input and shape files, you can use the JSONlab toolbox 
[4] to load and process in MATLAB.


Output data formats
------------------------------------

MCX may produces several output files depending user's simulation settings.
Overall, MCX produces two types of outputs, 1) data accummulated within the 
3D volume of the domain (volumetric output), and 2) data stored for each detected
photon (detected photon data).

### Volumetric output

By default, MCX stores a 4D array denoting the fluence-rate at each voxel in 
the volume, with a dimension of Nx*Ny*Nz*Ng, where Nx/Ny/Nz are the voxel dimension
of the domain, and Ng is the total number of time gates. The output data are
stored in the format of single-precision floating point numbers. One may choose
to output different physical quantities by setting the `-O` option. When the
flag `-X/--saveref` is used, the output volume may contain the total diffuse
reflectance only along the background-voxels adjacent to non-zero voxels. 
A negative sign is added for the diffuse reflectance raw output to distinguish
it from the fuence data in the interior voxels.

When photon-sharing (simultaneous simulations of multiple patterns) or photon-replay
(the Jacobian of all source/detector pairs) is used, the output array may be extended
to a 5D array, with the left-most/fastest index being the number of patterns Ns (in the
case of photon-sharing) or src/det pairs (in replay), denoted as Ns.

Several data formats can be used to store the 3D/4D/5D volumetric output. Starting
in MCX-CL v2023, JSON-based jnii format is the default format for saving the volumetric
data. Before v2023, the mc2 format is the default format.

#### jnii files

The JNIfTI format represents the next-generation scientific data storage 
and exchange standard and is part of the NeuroJSON initiative (https://neurojson.org)
led by the MCX author Dr. Qianqian Fang. The NeuroJSON project aims at developing
easy-to-parse, human-readable and easy-to-reuse data storage formats based on
the ubiquitously supported JSON/binary JSON formats and portable JData data annotation
keywords. In short, .jnii file is simply a JSON file with capability of storing 
binary strongly-typed data with internal compression and built in metadata.

The format standard (Draft 1) of the JNIfTI file can be found at

https://github.com/NeuroJSON/jnifti

A .jnii output file can be generated by using `-F jnii` in the command line.

The .jnii file can be potentially read in nearly all programming languages 
because it is 100% comaptible to the JSON format. However, to properly decode
the ND array with built-in compression, one should call JData compatible
libraries, which can be found at https://neurojson.org/wiki

Specifically, to parse/save .jnii files in MATLAB, you should use
- JSONLab for MATLAB (https://github.com/fangq/jsonlab) or install `octave-jsonlab` on Fedora/Debian/Ubuntu
- `jsonencode/jsondecode` in MATLAB + `jdataencode/jdatadecode` from JSONLab (https://github.com/fangq/jsonlab)

To parse/save .jnii files in Python, you should use
- PyJData module (https://pypi.org/project/jdata/) or install `python3-jdata` on Debian/Ubuntu

In Python, the volumetric data is loaded as a `dict` object where `data['NIFTIData']` 
is a NumPy `ndarray` object storing the volumetric data.


#### bnii files

The binary JNIfTI file is also part of the JNIfTI specification and the NeuroJSON
project. In comparison to text-based JSON format, .bnii files can be much smaller
and faster to parse. The .bnii format is also defined in the BJData specification

https://github.com/fangq/bjdata

and is the binary interface to .jnii. A .bnii output file can be generated by 
using `-F bnii` in the command line.

The .bnii file can be potentially read in nearly all programming languages 
because it was based on UBJSON (Universal Binary JSON). However, to properly decode
the ND array with built-in compression, one should call JData compatible
libraries, which can be found at https://neurojson.org/wiki

Specifically, to parse/save .jnii files in MATLAB, you should use one of
- JSONLab for MATLAB (https://github.com/fangq/jsonlab) or install `octave-jsonlab` on Fedora/Debian/Ubuntu
- `jsonencode/jsondecode` in MATLAB + `jdataencode/jdatadecode` from JSONLab (https://github.com/fangq/jsonlab)

To parse/save .jnii files in Python, you should use
- PyJData module (https://pypi.org/project/jdata/) or install `python3-jdata` on Debian/Ubuntu

In Python, the volumetric data is loaded as a `dict` object where `data['NIFTIData']` 
is a NumPy `ndarray` object storing the volumetric data.

#### mc2 files

The `.mc2` format is simply a binary dump of the entire volumetric data output,
consisted of the voxel values (single-precision floating-point) of all voxels and
time gates. The file contains a continuous buffer of a single-precision (4-byte) 
5D array of dimension Ns\*Nx\*Ny\*Nz\*Ng, with the fastest index being the left-most 
dimension (i.e. column-major, similar to MATLAB/FORTRAN).

To load the mc2 file, one should call `loadmc2.m` and must provide explicitly
the dimensions of the data. This is because mc2 file does not contain the data
dimension information.

Saving to .mc2 volumetric file is depreciated as we are transitioning towards
JNIfTI/JData formatted outputs (.jnii). 

#### nii files

The NIfTI-1 (.nii) format is widely used in neuroimaging and MRI community to
store and exchange ND numerical arrays. It contains a 352 byte header, followed
by the raw binary stream of the output data. In the header, the data dimension
information as well as other metadata is stored. 

A .nii output file can be generated by using `-F nii` in the command line.

The .nii file is widely supported among data processing platforms, including
MATLAB and Python. For example
- niftiread.m/niftiwrite in MATLAB Image Processing Toolbox
- JNIfTI toolbox by Qianqian Fang (https://github.com/NeuroJSON/jnifti/tree/master/lib/matlab)
- PyNIfTI for Python http://niftilib.sourceforge.net/pynifti/intro.html


### Detected photon data

If one defines detectors, MCX is able to store a variety of photon data when a photon
is captured by these detectors. One can selectively store various supported data fields,
including partial pathlengths, exit position and direction, by using the `-w/--savedetflag`
flag. The storage of detected photon information is enabled by default, and can be
disabled using the `-d` flag.

The detected photon data are stored in a separate file from the volumetric output.
The supported data file formats are explained below.

#### jdat files

By default, or when `-F jnii` is explicitly specified, a `.jdat` file is written, which is a
pure JSON file. This file contains a hierachical data record of the following JSON structure

````
 {
   "MCXData": {
       "Info":{
           "Version":
	   "MediaNum":
	   "DetNum":
	   ...
	   "Media":{
	      ...
	   }
       },
       "PhotonData":{
           "detid":
	   "nscat":
	   "ppath":
	   "mom":
	   "p":
	   "v":
	   "w0":
       },
       "Trajectory":{
           "photonid":
	   "p":
	   "w0":
       },
       "Seed":[
           ...
       ]
   }
 }
````
where "Info" is required, and other subfields are optional depends on users' input.
Each subfield in this file may contain JData 1-D or 2-D array constructs to allow 
storing binary and compressed data.

Although .jdat and .jnii have different suffix, they are both JSON/JData files and
can be opened/written by the same JData compatible libraries mentioned above, i.e.

For MATLAB
- JSONLab for MATLAB (https://github.com/fangq/jsonlab) or install `octave-jsonlab` on Fedora/Debian/Ubuntu
- `jsonencode/jsondecode` in MATLAB + `jdataencode/jdatadecode` from JSONLab (https://github.com/fangq/jsonlab)

For Python
- PyJData module (https://pypi.org/project/jdata/) or install `python3-jdata` on Debian/Ubuntu

In Python, the volumetric data is loaded as a `dict` object where `data['MCXData']['PhotonData']` 
stores the photon data, `data['MCXData']['Trajectory']` stores the trajectory data etc.


#### mch files

The .mch file, or MC history file, is stored by default, but we strongly encourage users
to adpot the newly implemented JSON/.jdat format for easy data sharing. 

The .mch file contains a 256 byte binary header, followed by a 2-D numerical array
of dimensions `#savedphoton * #colcount` as recorded in the header.
```
 typedef struct MCXHistoryHeader{
	char magic[4];                 // magic bits= 'M','C','X','H'
	unsigned int  version;         // version of the mch file format 
	unsigned int  maxmedia;        // number of media in the simulation 
	unsigned int  detnum;          // number of detectors in the simulation 
	unsigned int  colcount;        // how many output files per detected photon 
	unsigned int  totalphoton;     // how many total photon simulated 
	unsigned int  detected;        // how many photons are detected (not necessarily all saved) 
	unsigned int  savedphoton;     // how many detected photons are saved in this file 
	float unitinmm;                // what is the voxel size of the simulation 
	unsigned int  seedbyte;        // how many bytes per RNG seed
        float normalizer;              // what is the normalization factor
	int respin;                    // if positive, repeat count so total photon=totalphoton*respin; if negative, total number is processed in respin subset 
	unsigned int  srcnum;          // number of sources for simultaneous pattern sources 
	unsigned int  savedetflag;     // number of sources for simultaneous pattern sources 
	int reserved[2];               // reserved fields for future extension 
 } History;
```
When the `-q` flag is set to 1, the detected photon initial seeds are also stored
following the detected photon data, consisting of a 2-D byte array of `#savedphoton * #seedbyte`.

To load the mch file, one should call `loadmch.m` in MATLAB/Octave.

Saving to .mch history file is depreciated as we are transitioning towards
JSON/JData formatted outputs (`.jdat`).

### Photon trajectory data

For debugging and plotting purposes, MCX can output photon trajectories, as polylines,
when `-D M` or `-D T` flag is attached, or mcxlab is asked for the 5th output. Such information
can be stored in one of the following formats.

#### jdat files

By default, or when `-F jnii` is used, MCX-Cl merges the trajectory data with the detected photon and
seed data and saved as a JSON-compatible .jdat file. The overall structure of the
.jdat file as well as the relevant parsers can be found in the above section.

#### mct files

If `-F mc2` is used, MCX-CL stores the photon trajectory data in to a .mct file MC trajectory, which
uses the same binary format as .mch but renamed as .mct. This file can be loaded to
MATLAB using the same `loadmch.m` function. 

Using .mct file is depreciated and users are encouraged to migrate to .jdat file
as described below.

Using mcxlabcl in MATLAB and Octave
----------------------------

mcxlabcl is the native MEX version of MCX-CL for Matlab and GNU Octave. It includes
the entire MCX-CL code in a MEX function which can be called directly inside
Matlab or Octave. The input and output files in MCX-CL are replaced by convenient
in-memory struct variables in mcxlabcl, thus, making it much easier to use
and interact. Matlab/Octave also provides convenient plotting and data
analysis functions. With mcxlabcl, your analysis can be streamlined and speed-
up without involving disk files.

Please read the `mcxlab/README` file for more details on how to
install and use MCXLAB.

Specifically, please add the path to `mcxlabcl.m` and `mcxcl.mex*` to your
MATLAB or octave following Step 7 in Section II Installation.

To use `mcxlabcl`, the first step is to see if your system has any supported
processors. To do this, you can use one of the following 3 ways

	info=mcxlabcl('gpuinfo')

or run

	info=mcxlab('gpuinfo','opencl')

or

	USE_MCXCL=1
	info=mcxlab('gpuinfo')

Overall, mcxlabcl and mcxlab is highly compatible with nearly identical features
and interfaces. If yo have a working mcxlab script, the simpliest way to use it
with mcxlabcl on non-NVIDIA devices is to insert the below command

	eval('base','USE_MCXCL=1');

at the begining of the script, and insert 

	eval('base','USE_MCXCL=0');

at the end of the script.

If you have supported processors, please then run the demo mcxlabcl scripts
inside mcxlabcl/examples. mcxlabcl and mcxlab has a high compatibility in interfaces
and features. If you have a previously written MCXLAB script, you are likely able
to run it without modification when calling `mcxlabcl`. All you need to do
is to define

	USE_MCXCL=1
 
in matlab's base workspace (command line window). Alternatively, you may replace
`mcxlab()` calls by `mcxlab(...,'opencl')`, or by `mcxlabcl()`.

Please make sure you select the fastest processor on your system by using the `cfg.gpuid`
field. 


Using PMCX-CL in Python
------------------------------------

PMCX-CL is the native binary binding of MCX-CL for Python 3.6 or newer. Similar to
MCXLAB, PMCX-CL can run GPU-based simulations inside Python environment with
efficient in-memory inputs and outputs. 

Please read the pmcxcl/README.txt file for more details on how to install and 
use PMCX-CL.

Please also browse this interactive [Jupyter Notebook based PMCX-CL tutorial](https://colab.research.google.com/github/fangq/mcx/blob/master/pmcx/tutorials/pmcx_getting_started.ipynb)
to see a suite of examples showing the key functionalities of PMCX-CL.

Using MCXStudio GUI
----------------------------

MCXStudio is a graphics user interface (GUI) for MCX/MCXCL and MMC. It gives users
a straightforward way to set the command line options and simulation
parameters. It also allows users to create different simulation tasks 
and organize them into a project and save for later use.
MCX Studio can be run on many platforms such as Windows,
GNU Linux and Mac OS.

To use MCXStudio, it is suggested to put the mcxstudio binary
in the same directory as the mcx command; alternatively, you can
also add the path to mcx command to your PATH environment variable.

Once launched, MCX Studio will automatically check if mcx/mcxcl
binary is in the search path, if so, the "GPU" button in the 
toolbar will be enabled. It is suggested to click on this button
once, and see if you can see a list of GPUs and their parameters 
printed in the output field at the bottom part of the window. 
If you are able to see this information, your system is ready
to run MCX simulations. If you get error messages or not able
to see any usable GPU, please check the following:

* are you running MCX Studio/MCX on a computer with a supported card?
* have you installed the CUDA/NVIDIA drivers correctly?
* did you put mcx in the same folder as mcxstudio or add its path to PATH?

If your system has been properly configured, you can now add new simulations 
by clicking the "New" button. MCX Studio will ask you to give a session
ID string for this new simulation. Then you are allowed to adjust the parameters
based on your needs. Once you finish the adjustment, you should click the 
"Verify" button to see if there are missing settings. If everything looks
fine, the "Run" button will be activated. Click on it once will start your
simulation. If you want to abort the current simulation, you can click
the "Stop" button.

You can create multiple tasks with MCX Studio by hitting the "New"
button again. The information for all session configurations can
be saved as a project file (with .mcxp extension) by clicking the
"Save" button. You can load a previously saved project file back
to MCX Studio by clicking the "Load" button.


Interpreting the Output
----------------------------

MCX/MCX-CL output consists of two parts, the flux volume 
file and messages printed on the screen.

##### Output files

A `.jnii` file contains the fluence-rate distribution from the simulation in 
the given medium. By default, this fluence-rate is a normalized solution 
(as opposed to the raw probability) therefore, one can compare this directly 
to the analytical solutions (i.e. Green's function). The order of storage in the 
`.jnii` files is the same as the input file: i.e., if the input is row-major, the 
output is row-major, and so on. The dimensions of the file are Nx, Ny, Nz, and Ng
where Ng is the total number of time gates.

By default, MCX produces the **Green's function** of the 
**fluence rate**  for the given domain and source. Sometime it is also 
known as the time-domain "two-point" function. If you run MCX with the following command

	mcxcl -f input.json -s output ....

the fluence-rate data will be saved in a file named `output.dat` under
the current folder. If you run MCX without `-s output`, the
output file will be named as `input.json.dat`.

To understand this further, you need to know that a **fluence-rate `Phi(r,t)`** is
measured by number of particles passing through an infinitesimal 
spherical surface per **unit time** at **a given location** regardless of directions.
The unit of the MCX output is <sup>W</sup>&frasl;<sub>mm<sup>2</sup> s </sub> = <sup>J</sup>&frasl;<sub>mm<sup>2</sup>s</sub>, if it is interpreted as the 
"energy fluence-rate" [6], or <sup>1</sup>&frasl;<sub>mm<sup>2</sup>s </sub>, if the output is interpreted as the 
"particle fluence-rate" [6].

The Green's function of the fluence-rate means that it is produced
by a **unitary source**. In simple terms, this represents the 
fraction of particles/energy that arrives a location per second 
under **the radiation of 1 unit (packet or J) of particle or energy 
at time t=0**. The Green's function is calculated by a process referred
to as the "normalization" in the MCX code and is detailed in the 
MCX paper [6] (MCX and MMC outputs share the same meanings).

Please be aware that the output flux is calculated at each time-window 
defined in the input file. For example, if you type 

	0.e+00 5.e-09 1e-10  # time-gates(s): start, end, step

in the 5th row in the input file, MCX will produce 50 fluence-rate
snapshots, corresponding to the time-windows at [0 0.1] ns, 
[0.1 0.2]ns ... and [4.9,5.0] ns. To convert the fluence rate
to the fluence for each time-window, you just need to
multiply each solution by the width of the window, 0.1 ns in this case. 
To convert the time-dependent fluence-rate to continuous-wave (CW) 
fluence (fluence in short), you need to integrate the
fluence-rate along the time dimension. Assuming the fluence-rate after 
5 ns is negligible, then the CW fluence is simply `sum(flux_i*0.1 ns, i=1,50)`. 
You can read `mcx/examples/validation/plotsimudata.m`
and `mcx/examples/sphbox/plotresults.m` for examples 
to compare an MCX output with the analytical fluence-rate/fluence solutions.

One can load an `mc2` output file into Matlab or Octave using the
`loadmc2` function in the `{mcx root}/utils` folder. 

To get a continuous-wave solution, run a simulation with a sufficiently 
long time window, and sum the flux along the time dimension, for 
example

	fluence=loadmc2('output.mc2',[60 60 60 10],'float');
	cw_mcx=sum(fluence,4);

Note that for time-resolved simulations, the corresponding solution
in the results approximates the flux at the center point
of each time window. For example, if the simulation time window 
setting is [t<sub>0</sub>,t<sub>0</sub>+dt,t<sub>0</sub>+2dt,t<sub>0</sub>+3dt...,t1], the time points for the 
snapshots stored in the solution file is located at 
[t<sub>0</sub>+dt/2, t<sub>0</sub>+3*dt/2, t<sub>0</sub>+5*dt/2, ... ,t<sub>1</sub>-dt/2]

A more detailed interpretation of the output data can be found at 
<http://mcx.sf.net/cgi-bin/index.cgi?MMC/Doc/FAQ#How_do_I_interpret_MMC_s_output_data>

MCX can also output "current density" (`J(r,t)`, unit <sup>W</sup>&frasl;<sub>m<sup>2<sup></sub>, same as 
`Phi(r,t)`) - referring to the expected number of photons or Joule of energy 
flowing through a unit area pointing towards a particular direction per unit
time. The current density can be calculated at the boundary of the domain by 
two means:

1. using the detected photon partial path output (i.e. the second output of 
 mcxlab.m), one can compute the total energy `E` received by a detector, then one 
 can divide E by the area/aperture of the detector to obtain the `J(r)` at a 
 detector (E should be calculated as a function of t by using the time-of-fly of 
 detected photons, the `E(t)/A` gives `J(r,t)`; if you integrate all time gates, 
 the total E/A gives the current I(r), instead of the current density).
2. use `-X 1 or --saveref/cfg.issaveref` option in mcx to enable the diffuse 
 reflectance recordings on the boundary. the diffuse reflectance is represented 
 by the current density `J(r)` flowing outward from the domain.

The current density has, as mentioned, the same unit as fluence rate, but the 
difference is that `J(r,t)` is a vector, and `Phi(r,t)` is a scalar. Both 
measuring the energy flow across a small area (the are has direction in the 
case of J) per unit
time.

You can find more rigorous definitions of these quantities in Lihong Wang's 
Biomedical Optics book, Chapter 5.

##### Guide for output units

- Flux (<sup>W</sup> &frasl; <sub> mm<sup>2</sup> J </sub>): Normalized energy flux/fluence-rate. 
Or (<sup>1</sup> &frasl; <sub> mm<sup>2</sup> s </sub>) which is the normalized particle fluence-rate.

- Fluence (<sup>J</sup> &frasl; <sub> mm<sup>2</sup> J </sub>): Normalized energy fluence. 
Or (<sup>1</sup> &frasl; <sub> mm<sup>2</sup> </sub>) which is the normalized particle fluence.
	
- Energy (<sup>J</sup> &frasl; <sub> mm<sup>2</sup> J </sub>): Normalized energy deposition. 
Or (<sup>1</sup> &frasl; <sub> mm<sup>3</sup> </sub>) which is the normalized particle deposition.

For example if one wanted an image of the fluence through their system 
(such that `--outputtype` is `fluence`) you would multiply the output from `mcxcl` by
the amount of joules delivered. 

##### Console print messages

Timing information is printed on the screen (stdout). The clock starts (at time 
T0) right before the initialization data is copied from CPU to GPU. For each 
simulation, the elapsed time from T0 is printed (in ms). Also the accumulated 
elapsed time is printed for all memory transaction from GPU to CPU.

When a user specifies `-D P` in the command line, or set `cfg.debuglevel='P'`, 
MCXCL or MCXLABCL prints a progress bar showing the percentage of completition.

When a user adds `-J "-DMCX_GPU_DEBUG"` in the command line, MCXCL prints
step-by-step photon movement information for easy debugging. Please restrict the photon
to a small number, such as `-n 1` to avoid long output.


Best practices guide
----------------------------

To maximize MCX-CL's performance on your hardware, you should follow the best 
practices guide listed below:

**Use dedicated GPUs:** A dedicated GPU is a GPU that is not connected to a monitor. If you use
a non-dedicated GPU, any kernel (GPU function) can not run more than a
few seconds. This greatly limits the efficiency of MCX. To set up a 
dedicated GPU, it is suggested to install two graphics cards on your 
computer, one is set up for displays, the other one is used for GPU 
computation only. If you have a dual-GPU card, you can also connect 
one GPU to a single monitor, and use the other GPU for computation
(selected by `-G` in mcx/mcxcl). If you have to use a non-dedicated GPU, you
can either use the pure command-line mode (for Linux, you need to 
stop X server), or use the `-r` flag to divide the total simulation 
into a set of simulations with less photons, so that each simulation 
only lasts a few seconds.

**Launch as many threads as possible:** It has been shown that MCX-CL's 
speed is related to the thread number (`-t`).
Generally, the more threads, the better speed, until all GPU resources
are fully occupied. For higher-end GPUs, a thread number over 10,000 
is recommended. Please use the autopilot mode, `-A`, to let MCX determine
the "optimal" thread number when you are not sure what to use.


Acknowledgement
------------------

MCX contains modified versions of the below source codes from other 
open-source projects (with a compatible license).

### cJSON library by Dave Gamble

- Files: src/cJSON folder
- Copyright (c) 2009 Dave Gamble
- URL: https://github.com/DaveGamble/cJSON
- License: MIT License, https://github.com/DaveGamble/cJSON/blob/master/LICENSE

### ZMat data compression unit

- Files: src/zmat/*
- Copyright: 2019-2020 Qianqian Fang
- URL: https://github.com/fangq/zmat
- License: GPL version 3 or later, https://github.com/fangq/zmat/blob/master/LICENSE.txt

### LZ4 data compression library

- Files: src/zmat/lz4/*
- Copyright: 2011-2020, Yann Collet
- URL: https://github.com/lz4/lz4
- License: BSD-2-clause, https://github.com/lz4/lz4/blob/dev/lib/LICENSE

### LZMA/Easylzma data compression library

- Files: src/zmat/easylzma/*
- Copyright: 2009, Lloyd Hilaiel, 2008, Igor Pavlov
- License: public-domain
- Comment:
 All the cruft you find here is public domain.  You don't have to
 credit anyone to use this code, but my personal request is that you mention
 Igor Pavlov for his hard, high quality work.

### myslicer toolbox by Anders Brun

- Files: utils/{islicer.m, slice3i.m, image3i.m}
- Copyright (c) 2009 Anders Brun, anders@cb.uu.se
- URL: https://www.mathworks.com/matlabcentral/fileexchange/25923-myslicer-make-mouse-interactive-slices-of-a-3-d-volume
- License: BSD-3-clause License, https://www.mathworks.com/matlabcentral/fileexchange/25923-myslicer-make-mouse-interactive-slices-of-a-3-d-volume#license_modal


Reference
----------------------------

- [1] Leiming Yu, Fanny Nina-Paravecino, David Kaeli, and Qianqian Fang, 
"Scalable and massively parallel Monte Carlo photon transport simulations 
for heterogeneous computing platforms", J. Biomed. Optics, 23(1), 010504 (2018) .
- [2] Qianqian Fang and David A. Boas, "Monte Carlo Simulation of Photon 
Migration in 3D Turbid Media Accelerated by Graphics Processing Units," 
Optics Express, vol. 17, issue 22, pp. 20178-20190 (2009).

If you used MCX in your research, the authors of this software would like
you to cite the above paper in your related publications.

**Links:** 

- [1] http://www.nvidia.com/object/cuda_get.html
- [2] http://www.nvidia.com/object/cuda_learn_products.html
- [3] http://en.wikipedia.org/wiki/Row-major_order
