Contributing to plotly.js

Opening issues

Please read the issue guidelines.

Making pull requests

Please read the pull request guidelines.

GitHub labels

We use the following labels to track issues and PRs:

Label	Purpose
`type: bug`	bug report confirmed by a plotly team member
`type: regression`	bug that introduced a change in behavior from one version to the next
`type: feature`	planned feature additions
`type: new trace type`	subset of `type: feature` reserved for planned new trace types
`type: translation`	localization-related tasks
`type: performance`	performance related tasks
`type: maintenance`	source code cleanup resulting in no enhancement for users
`type: documentation`	API doc or attribute description improvements
`type: community`	issue left open for community input and pull requests
`type: duplicate`	self-explanatory
`type: wontfix`	self-explanatory
`status: discussion needed`	Issue or PR that required discussion among maintainers before moving forward
`status: in progress`	PRs that required some initial feedback but not ready to merge
`status: reviewable`	PRs that are completed from the author's perspective
`status: on hold`	PRs that are put on hold

Development

Prerequisites

git
node.js. We recommend using node.js v12.x (LTS). Upgrading and managing node versions can be easily done using nvm or its Windows alternatives.
npm v6.x and up to ensure that the package-lock.json file is used and updated correctly.

Step 1: Clone the plotly.js repo and install its dependencies

git clone https://github.com/plotly/plotly.js.git
cd plotly.js
npm install

Step 2: Setup test environment

npm run pretest

Step 3: Start the test dashboard

npm start

This command bundles up the source files with source maps using browserify, starts a watchify file watcher (making the your dev plotly.js bundle update every time a source file is saved) and opens up a tab in your browser.

Step 4: Open up the console and start developing

A typical workflow is to make some modifications to the source, update the test dashboard, inspect and debug the changes, then repeat. The test dashboard comes bundled with some useful tools while developing - all bundled under the Tabs object:

Method/Property	Description
`Tabs.fresh([id])`	Creates a fresh graph div and returns it (default id of `graph`).
`Tabs.getGraph([id])`	Returns the default or specified graph div.
`Tabs.plotMock(mock, [id])`	Plots the specified mock (`.json` extension is not required).
`Tabs.snapshot([id])`	Creates a png snapshot of the plot and places it below.
`Tabs.reload()`	Reloads the plotly.js script and will execute `Tabs.onReload` once completed.
`Tabs.onReload()`	By default, set to `noop` but you may set `Tabs.onReload` to any function you wish. This is useful for replotting a mock or test every time you reload the plotly.js script.
`Tabs.purge()`	Destroys all plots.

View the source for more info.

Three additional helpers exist that are refreshed every second:

gd - this is the default plot div
fullData - shortcut to gd._fullData
fullLayout - shortcut to gd._fullLayout

There is also a search bar in the top right of the dashboard. This fuzzy-searches image mocks based on their file name and trace type.

Alternative to test dashboard

Use the plotly-mock-viewer which has live-reloading and a bunch of other cool features. An online version of plotly-mock-viewer is available at https://rreusser.github.io/plotly-mock-viewer/ which uses https://cdn.plot.ly/plotly-latest.min.js

Other npm scripts

npm run preprocess: pre-processes the css and svg source file in js. This script must be run manually when updating the css and svg source files.
npm run watch: starts a watchify file watcher just like the test dashboard but without booting up a server.

Testing

Both jasmine and image tests are run on CircleCI on every push to this repo.

Jasmine tests

Jasmine tests are run in a browser using karma. To run them locally:

npm run test-jasmine

To run a specific suite, use:

npm run test-jasmine -- <suite>

where the <suite> corresponds to the suite's file name as found in test/jasmine/tests/.

You can also test multiple suites at a time, for example:

npm run test-jasmine -- bar axes scatter

which will run tests in the bar_test.js, axes_test.js and scatter_test.js suites.

To turn off the autoWatch / auto-bundle / multiple run mode:

npm run test-jasmine -- <suite> --nowatch

In certain situations, you may find that the default reporting is not verbose enough to pin down the source of the failing test. In this situation, you may wish to use karma-verbose-reporter:

npm run test-jasmine -- <suite> --verbose

For more info on the karma / jasmine CLI:

npm run test-jasmine -- --help
npm run test-jasmine -- --info

Image pixel comparison tests

Image pixel comparison tests are run in a docker container. For more information on how to run them locally, please refer to image test README.

Running the test locally outputs the generated png images in build/test_images/ and the png diffs in build/test_images_diff/ (two git-ignored directories).

To view the image pixel comparison test results, run

npm run start-image_viewer

which shows the baseline image, the generated image, the diff and the json mocks of test cases that failed.

To view the results of a run on CircleCI, download the build/test_images/ and build/test_images_diff/ artifacts into your local repo and then run npm run start-image_viewer.

Using the developer console in karma to write/debug jasmine tests

Click on the DEBUG button
In the DEBUG RUNNER window, open the console (e.g. with <ctrl-shift-j>)
Find test file (e.g. with <ctrl-o> + typing the name of the file), look out for "bundled" files with the same name.
Set debugger on relevant line(s)
Rerun the test suite by refreshing the page (e.g. with <crtl-r>)

Writing jasmine interaction tests

Keep in mind that the interaction coordinates are relative to the top-left corner of the plot, including the margins. To produce a reliable interaction test, it may be necessary to fix the width, height, margins, X axis range and Y axis range of the plot. For example:

Plotly.newPlot(gd, [{
    x: [1, 1, 1, 2, 2, 2, 3, 3, 3],
    y: [1, 2, 3, 1, 2, 3, 1, 2, 3],
    mode: 'markers'
}], {
    width: 400, height: 400,
    margin: {l: 100, r: 100, t: 100, b: 100},
    xaxis: {range: [0, 4]},
    yaxis: {range: [0, 4]}
});

This will produce the following plot, and say you want to simulate a selection path of (175, 175) to (225, 225):

Repo organization

Distributed files are in dist/
CommonJS require-able modules are in lib/
Sources files are in src/
Build and repo management scripts are in tasks/
All tasks can be run using npm run-script
Tests are test/, they are partitioned into image and jasmine tests
Test dashboard and image viewer code is in devtools/
Built files are in build/ (the files in here are git-ignored, except for plotcss.js)

Trace module design

The trace modules (found in src/traces) are defined as plain objects with functions and constants attached to them in an index file (e.g. src/traces/scatter/index.js). The trace modules are "registered" undo the Registry object (found in src/registry.js) using Plotly.register (as done in the index files in dist/).

The trace module methods are meant to be called as part of loops during subplot-specific (e.g. in plots/cartesian/index.js) and figure-wide (e.g. in plots/plots.js) subroutines. That way, the subroutines work no matter which trace modules got registered.

All traces modules set:

_module.name: name of the trace module as used by the trace type attribute.
_module.basePlotModule: base plot (or subplot) module corresponding to the trace type (e.g. scatter links to the Cartesian base plot module, scatter3d links to gl3d).
_module.attributes: JSON-serializable object of attribute declarations. This object is used to generate the plot-schema JSON.
_module.supplyDefaults: Takes in input trace settings and coerces them into "full" settings under gd._fullData. This one is called during the figure-wide Plots.supplyDefaults routine. Note that the supplyDefaults method performance should scale with the number of attributes (not the number of data points - so it should not loop over any data arrays).
_module.calc: Converts inputs data into "calculated" (or sanitized) data. This one is called during the figure-wide Plots.doCalcdata routine. The calc method is allowed to scale with the number of data points and is in general more costly than supplyDefaults. Please note that some edit pathways skip Plots.doCalcdata (as determined by the editType flags in the attributes files).
_module.plot: Draws the trace on screen. This one is called by the defined basePlotModule.

Other methods used by some trace modules:

_module.categories: list of string identifiers used to group traces by behavior. Traces that have a given category can then be detected using Registry.traceIs
_module.layoutAttributes: JSON-serializable object of attribute declarations coerced in the layout (e.g. barmode for bar traces)
_module.supplyLayoutDefaults: Defaults logic for layout attributes.
_module.crossTraceDefaults: Defaults logic that depends on input setting of multiple traces.
_module.crossTraceCalc: Computations that depend on the data of multiple traces.
_module.colorbar: Defines the colorbar appearance for traces that support it.
_module.hoverPoints: Point-picking logic called during hover.
_module.selectPoints: Polygon-containing logic called during selections.
_module.style: Sometimes split from _module.plot where _module.plot only draws the elements and _module.style styles them.
_module.styleOnSelect: Optimization of _module.style called during selections.
_module.convert: Sometimes separated from _module.plot or _module.calc to convert the plotly.js settings to another framework e.g. to gl-plot3d for gl3d traces, to mapbox-gl from mapbox traces. This split can make the logic easier to test. If you make a convert, you should call it from either calc or plot.

Coding style

Check if ok, with npm run lint

See eslintrc and the eslint list of rules for more details.
Rules listed in the eslintrc file with the ignore flag 0 are the recommended rules for new code added.

Uh oh!