Reference for different experiment options

Value sweeping runs a simulation for each of the specified `values`

. The `field`

in **globals.json** is populated with the value for each run.

"Radius values": {"steps": 100,"type": "values","field": "radius","values": [0, 1, 2, 3, 4, 5, 6, 7]}

Value sweeping is particularly is useful for multi-parameter sweeps and categorical sampling.

Fixed sample sweeping or 'linspace' is one of the most common types of parameter sweeps. Define `start`

, `stop`

, and number of `samples`

to generate an even sampling between two values with a set number of data points.

"Radius fixed sample": {"steps": 100,"type": "linspace","field": "radius","start": 0,"stop": 10,"samples": 11}

Instead of using a set number of samples like linspace, arange samples every "increment" between the specified start and stop fields.

"Radius arange": {"steps": 100,"type": "arange","field": "radius","start": 0,"stop": 10,"increment": 0.5}

Monte Carlo sweeping allows random sampling from a custom distribution. Each supported distribution can be customized through the associated parameters. Each parameter defaults to 1 if not defined.

"Radius monte": {"steps": 100,"type": "monte-carlo","field": "radius","samples": 10,â€‹// Either combination of distributions and parameters:"distribution": "normal","mean": 1,"std": 1â€‹// or"distribution": "log-normal","mu": 1,"sigma": 1â€‹// or"distribution": "poisson","rate": 1â€‹// or"distribution": "beta","alpha": 1,"beta": 1â€‹// or"distribution": "gamma","shape": 1,"scale": 1}

You can run groups of experiments together by adding experiment keys to the `runs`

array of a `group`

definition. The below code, for example, would execute each of our experiments outlined above as sub-experiments of a new experiment:

"Group Sweep": {"steps": 100,"type": "group","runs": ["Radius values", "Radius linspace", "Radius arange", "Radius monte"]}

In order to discover interaction effects in your model, you'll have to perform sweeps over multiple parameters. The multiparameter experiment generates a full factorial design with all of the experiments defined in `runs`

.

"Full factorial sweep": {"steps": 100,"type": "multiparameter","runs": ["Radius values","Radius linspace","Radius arange","Radius monte"]}

Optimization experiments allow you to identify the best combination of parameters for minimizing or maximizing desired metrics.

Read more about Optimization Experiments in the next section of the docs.