Using the interfaces

A call to g2s is needed to launch any computation. Each call to g2s is composed of parameters of G2S and of the name of the algorithm used. Flags do NOT have a specific order.

Use g2s in Python

from g2s import g2s
data = g2s(...)  # it returns a tuple that contains all the output maps and the computing duration

You can either write '-flag',value or flag=value or a mix of both.

Use g2s in MATLAB

data = g2s(...)          % principal output, the simulation
[data, t] = g2s(...)     % the simulation and the computation time
[data, ...,t] = g2s(...) % the simulation, other outputs map and the computation time

A call to g2s is needed to launch any computation. Each call to g2s is composed of parameters of G2S and of the name of the algorithm used. Flags do NOT have a specific order. You can either write '-flag',value or flag=value or a mix of both.

Use g2s in R

An R interface is available in the repository. Please refer to the installation page for the current build instructions.

Inteface flags

Flag	Description
--version	Return the version and compilation date of the interface
-a	The simulation algorithm to be used, it can be ‘qs’, ‘nds’, ‘ds-l’ (DS-like, not maintained)
-sa	Server address (default: localhost (the server is local), otherwise provide IP address). Nice when we have a powerful machine dedicated for computation
-p	Port where to look for the server (default: 8128). Should be passed as an integer.
-silent	Don’t display the progression, useful for scripts
-serverStatus	Query the server status without submitting a job. Typical return values are negative for communication errors, 0 for an undefined status, and positive values when the server is operational.
-noTO	Deactivate the communication timeout on the client side. Useful for slow or unstable network connections.
-TO	Specify a custom communication timeout in milliseconds for the client side ZMQ socket.
-shutdown	Shutdown the server, useful at the end of scripts

For distributed QuickSampling, the Python and MATLAB interfaces also accept native -jg / -job_grid matrices. They automatically convert these matrices to the JSON payload expected by the distributed helpers, and the interface-side normalization prefers the short flag -jg.

For Anchor Sampling continuous norms (-cnorm / -cn), Python and MATLAB can pass either a scalar (single p for all continuous variables) or a vector (one p per continuous variable).

Asynchronous mode flags

The following options represent the Asynchronous mode, which allows you to submit multiple jobs simultaneously and retrieve the results of each of them later on (as opposed to synchronous communication with the server, where you need to wait until a job is finished before you can submit a new one). You launch the async mode by simply adding the -submitOnly flag to your g2s call. This will give only the job ID as an output, so the g2s call becomes jobid = g2s(flag1,value1, flag2,value2, ...). Don’t forget to always include the server address if it’s not local! See the example section for a demonstration in MATLAB and Python.

Flag	Description
-submitOnly	Submit a job and return only the job id.
-statusOnly	Check progression Usage: status = g2s('-statusOnly',jobid)
-waitAndDownload	Download the result Usage: sim,_ = g2s('-waitAndDownload',jobid)
-kill	Kill a given task Usage: g2s('-kill',jobid)
-after	Add one or more job dependencies. The submitted job starts only after the specified job id(s) are finished. Use '-after',0 to explicitly allow independent jobs to run in parallel through the G2S dependency manager.