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Formalizing Visualization Design Knowledge as Constraints

Test Coverage Status PyPi Code style: black code style: prettier

Draco is a formal framework for representing design knowledge about effective visualization design as a collection of constraints. You can use Draco to find effective visualization visual designs in Vega-Lite. Draco's constraints are implemented in based on Answer Set Programming (ASP) and solved with the Clingo constraint solver. We also implemented a way to learn weights for the recommendation system directly from the results of graphical perception experiment.

Read our introductory blog post about Draco and our research paper for more details. Try Draco in the browser at

We are working on an improved version of Draco at


There Be Dragons! This project is in active development and we are working hard on cleaning up the repository and making it easier to use the recommendation model in Draco. If you want to use this right now, please talk to us. More documentation is forthcoming.


This repository currently contains:

  • draco (pypi) The ASP programs with soft and hard constraints, a python API for running Draco, the CLI, and the python wrapper for the draco-core API. Additionally includes some helper functions that may prove useful.
  • draco-core (npm) Holds a Typescript / Javascript friendly copy of the ASP programs, and additionally, a Typescript /Javascript API for all the translation logic of Draco, as described below.

Sibling Repositories

Various functionality and extensions are in the following repositories

  • draco-vis

    • A web-friendly Draco! Including a bundled Webassembly module of Draco's solver, Clingo.
  • draco-learn

    • Runs a learning-to-rank method on results of perception experiments.
  • draco-tools

    • UI tools to create annotated datasets of pairs of visualizations, look at the recommendations, and to explore large datasets of example visualizations.
  • draco-analysis

    • Notebooks to analyze the results.

Draco API (Python)

In addition to a wrapper of the Draco-Core API describe below, the python API contains the following functions.

object Result <>

The result of a Draco run, a solution to a draco_query. User result.as_vl() to convert this solution into a Vega-Lite specification.

run (draco_query: List[str] [,constants, files, relax_hard, silence_warnings, debug, clear_cache]) -> Result: <>

Runs a draco_query, defined as a list of Draco ASP facts (strings), against given file asp programs (defaults to base Draco set). Returns a Result if the query is satisfiable. If relax_hard is set to True, hard constraints (hard.lp) will not be strictly enforced, and instead will incur an infinite cost when violated.

is_valid (draco_query: List[str] [,debug]) -> bool: <>

Runs a draco_query, defined as a list of Draco ASP facts (strings), against Draco's hard constraints. Returns true if the visualization defined by the query is a valid one (does not violate hard constraints), and false otherwise. Hard constraints can be found in hard.lp.

data_to_asp (data: List) -> List[str]: <>

Reads an array of data and returns the ASP declaration of it (a list of facts).

read_data_to_asp (file: str) -> List[str]: <>

Reads a file of data (either .json or .csv) and returns the ASP declaration of it (a list of facts).

Draco-Core API (Typescript / Javascript)

vl2asp (spec: TopLevelUnitSpec): string[] <>

Translates a Vega-Lite specification into a list of ASP Draco facts.

cql2asp (spec: any): string[] <>

Translates a CompassQL specification into a list of ASP Draco constraints.

asp2vl (facts: string[]): TopLevelUnitSpec <>

Interprets a list of ASP Draco facts as a Vega-Lite specification.

data2schema (data: any[]): Schema <>

Reads a list of rows and generates a data schema for the dataset. data should be given as a list of dictionaries.

schema2asp (schema: Schema): string[] <>

Translates a data schema into an ASP declaration of the data it describes.

constraints2json (constraintsAsp: string, weightsAsp?: string): Constraint[] <>

Translates the given ASP constraints and matching weights (i.e. for soft constraints) into JSON format.

json2constraints (constraints: Constraint[]): ConstraintAsp <>

Translates the given JSON format ASP constraints into ASP strings for definitions and weights (if applicable, i.e. for soft constraints).

User Info


Python (Draco API)

Install Clingo

You can install Clingo with conda: conda install -c potassco clingo. On MacOS, you can alternatively run brew install clingo.

Install Draco (Python)

pip install draco

Typescript / Javascript (Draco-Core API)

STOP! If you wish to run Draco in a web browser, consider using draco-vis, which bundles the Clingo solver as a WebAssembly module. The Draco-Core API does not include this functionality by itself. It merely handles the logic of translating between the various interface languages.

yarn add draco-core or npm install draco-core

Developer Info


Install Clingo.

You can install Clingo with conda: conda install -c potassco clingo. On MacOS, you can alternatively run brew install clingo.

Install node dependencies

yarn or npm install

You might need to activate a Python 2.7 environment to compile the canvas module.

Build JS module

yarn build. We are currently using typescript version 3.2.1 and greater.

Python setup

pip install -r requirements.txt or conda install --file requirements.txt

Install Draco in editable mode. We expect Python 3.

pip install -e .

Now you can call the command line tool draco. For example draco --version or draco --help.


You should also be able to run the tests (and coverage report)

python test

Run only ansunit tests

ansunit asp/tests.yaml

Run only python tests

pytest -v

Test types

mypy draco tests --ignore-missing-imports

Running Draco

End to end example

To run Draco on a partial spec.

sh spec

The output would be a .vl.json file (for Vega-Lite spec) and a .png file to preview the visualization (by default, outputs would be in folder __tmp__).

Use CompassQL to generate examples

Run yarn build_cql_examples.

Run Draco directly on a set of ASP constraints

You can use the helper file asp/_all.lp.

clingo asp/_all.lp test.lp

Alternatively, you can invoke Draco with draco -m asp test.lp.

Run APT example

clingo asp/_apt.lp examples/example_apt.lp --opt-mode=optN --quiet=1 --project -c max_extra_encs=0

This only prints the relevant data and restricts the extra encodings that are being generated.


  • Make sure everything works!
  • Update __version__ in draco/ and use the right version below.
  • git commit -m "bump version to 0.0.1"
  • Tag the last commit git tag -a v0.0.1.
  • git push and git push --tags
  • Run python sdist upload.


Related Repositories

Previous prototypes

For a bit of historical perspective, after building Compass and CompassQL with @kanitw, @domoritz worked with colleagues at UW on a first constraint-based prototype vis-csp in ASP, then tried Z3 in vis-constraints before going back to ASP for Draco.

Related software