pymcx

Python package for MCX .

package information

functions

• loadmch(fname, format, endian, datadict) : load .mch file and output the detected photons data as an array or a dictionary.
• loadmc2(path, dimension) :
• create() : create a configuration variable as a python dictionary that fallow the rules of the json structure configuration file.
• run(cfg, flags, mcxbin) : run the mcx simulation with the configuration cfg and mcx flags

All the functions can be used separately.

How to use it

The way the package work is as fallow.

First use the create() function to generate a default,python dictionary, configuration parameter cfg. Then modify it to take in too account your simulation parameters. The dictionary fallow the same rules as the Json configuration file.

If needed you can create a flags variable to indicate the desired flags at run time. Example flags = "--repeat 2 --reflect 0"

To run the simulation, use the run() function as data = run(cfg,flags). The run function will create a Json file from the cfg dictionary and run the mcx binary as,

./mcx -f cfg.json *flags*

At the end of the simulation the run function will automatically read the .mch and .mc2 files that was generated by mcx.

Before running the simulation make sure that the mcx binary is at the root of your running python file. If not you can give the path to the mcx binary in to the run function as run(cfg, flag, mcxpath)

Example

import pymcx as mcx

cfg = mcx.create() #create a default config dictionary

cfg["Session"]["Photons"] = 1e6
cfg["Optode"]["Detector"] = [{"Pos": [29.0,  19.0,  0.0], "R": 1.0}]

data = mcx.run(cfg)

To have more information read the Json part of the MCX readme. The cfg variable is a python dictionary that look like a Json structure.