Griddler: a grammar of parameters

Griddler is a tool for converting human-written specifications for simulation experiments into lists of machine-readable specifications.

Why griddler?

Modelers often want to run multiple simulations with different but related parameterizations. Some parameters should be in common across all simulations, while others should vary in patterns like "grids" or "bundles."

Griddler provides a grammar of these kinds of multiple parameterizations that aims to separate the specification of parameterizations from the running of simulations. Ideally, a user can write a single "griddle" file (or, in more complex cases, a griddle-importing Python script) that produces all the different simulation parameterizations of interest.

Getting started

Read the GitHub pages documentation!

If you have a griddle written in a supported schema, the easiest way to use griddler is from the command line:

bash python -m griddler my_griddle.yaml > my_experiment.json

Or, from within Python:

```python import griddler import yaml

with open("mygriddle.yaml") as f: mygriddle = yaml.safe_load(f)

myexperiment = griddler.parse(mygriddle) myparametersets = myexperiment.todicts() ```

Examples

For complex experiments, you might want to write a Python file that manipulates Parameter, Spec, and Experiment objects directly. See the API reference for more details.

For simpler experiments, griddler supports multiple griddle schemas. A griddle is a human-written file, usually a YAML or JSON, that contains some metadata and then whatever is needed to uniquely specify the experiment. See the griddle for more details.

In the v0.4 schema, the trivial example is:

yaml schema: v0.4 experiment: []

The minimal example of a single, fixed parameter is:

yaml schema: v0.4 experiment: [{ R0: 1.0 }]

The output, serialized as JSON, is:

json [{ "R0": 1.0 }]

An example of the product might be:

yaml schema: v0.4 experiment: product: - [{ R0: 1.5 }, { R0: 2.0 }] - [{ gamma: 0.3 }, { gamma: 0.4 }]

which produces a list of 4 outputs, with all combinations of input varying parameters:

json [ { "R0": 1.5, "gamma": 0.3 }, { "R0": 1.5, "gamma": 0.4 }, { "R0": 2.0, "gamma": 0.3 }, { "R0": 2.0, "gamma": 0.4 } ]

Unions become useful when combining experiments that vary different parameters. For example, an experiment might consist of some simulations where a simulated quantity follows the normal distribution and other simulations where it follows the gamma distribution. For the normal distribution simulations, we might want to grid over values of the mean and standard deviation, while in the gamma distribution simulations, we want to grid over shape and scale parameters:

yaml schema: v0.4 experiment: product: - [{ R0: 1.5 }] - union: - product: - [{ distribution: normal }] - [{ mean: 0.5 }, { mean: 1.0 }, { mean: 1.5 }] - [{ sd: 0.5 }, { sd: 1.0 }] - product: - [{ distribution: gamma }] - [{ shape: 0.5 }, { shape: 1.0 }] - [{ scale: 0.5 }, { scale: 1.0 }]

which produces multiple outputs:

json [ { "R0": 1.5, "distribution": "normal", "mean": 0.5, "sd": 0.5 }, { "R0": 1.5, "distribution": "normal", "mean": 0.5, "sd": 1.0 }, { "R0": 1.5, "distribution": "normal", "mean": 1.0, "sd": 0.5 }, // etc. with further mean/sd combinations { "R0": 1.5, "distribution": "gamma", "shape": 0.5, "scale": 0.5 } // etc. with further shape/scale combinations ]

Project Admin

Scott Olesen ulp7@cdc.gov (CDC/IOD/ORR/CFA)

Changelog

See the repo releases.

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