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https://github.com/nomic-ai/deepscatter

Zoomable, animated scatterplots in the browser that scales over a billion points
https://github.com/nomic-ai/deepscatter

data-visualization visualization webgl

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Zoomable, animated scatterplots in the browser that scales over a billion points

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README 

        
# Deep Scatterplots for the Web



This is a library for allowing interactive visualization of extremely large datasets, in browser.



It's fast for three reasons:



1. All data is sent in the Apache Arrow `feather` format, in a

   custom quadtree format that makes it possible to only load

   data as needed on zoom. Feather takes no time to parse in the browser

   once transferred, compresses pretty well, and can be directly copied to the GPU without

   transformation in JS. This is [the way of the future.](https://benschmidt.org/post/2020-01-15/2020-01-15-webgpu/)

   We also now allow custom in-browser calculations on Arrow data.

2. Most rendering is done in custom layers using WebGL, with a

   buffer management strategy handled by REGL. This means that

   there are no unnecessary abstractions around points or separate draw calls

   for different objects; a minimum number of buffers are attached for all

   needed points.

3. Almost all grammar-of-graphics transforms such are handled on the GPU,

   which allows for interpolated transitions with calculations

   done in parallel.



It also runs in completely static settings, so you can host a million-point scatterplot over something like Github Pages.



Deepscatter is the underlying graphics engine for [Atlas](https://atlas.nomic.ai). It is provided under an NC-CC-BY-SA license for all noncommercial use.



# Examples



- [Atlas map of 5.5 million tweets](https://atlas.nomic.ai/map/twitter). (Nomic)

- [Narrative visualization of 20 million biomedical abstracts embedded using T-SNE.](https://static.nomic.ai/pubmed.html). (Collaboration between Nomic and the University of Tübingen).

- [1 million+ documents from arxiv.com](https://observablehq.com/@bmschmidt/arxiv) rendered inside an Observable notebook. (Ben Schmidt)

- [Every person in the 2010 and 2020 US Censuses](https://all-of-us.benschmidt.org) displayed in an interactive svelte-kit app. (Ben Schmidt)

- [Newspaper Articles at the Library of Congress from the Reconstruction Era](https://situating.us/explore). (Andromeda Yelton while in residency at the Library of Congress).



# Documentation



See [the autogenerated typescript documentation](https://nomic-ai.github.io/deepscatter/) for a full list of methods and functions.



# Get help



Github issues, even low quality ones, are welcome here.



There is also a dedicated [Deepscatter Slack](https://join.slack.com/t/deepscatter/shared_invite/zt-17kbudjhj-zVzt26zddEpSyACe2E71Fw) which you are welcome to join. Nomic maintains a [discord server](https://discord.gg/4M2QFmTt2k) that has dedicated Deepscatter and Atlas support channels.



I came into doing this stuff from a very non-technical background and welcome people to join with naive questions.



# Quick start



## Importing the module.



See the [arxiv example above](https://observablehq.com/@bmschmidt/arxiv) to see some basic examples.



## Running locally.



First, install the companion tiling library, which is written in python,

and generate a million points of test data in tiles of 50000 apiece.



```sh

python3 -V # requires Python 3.9.x or 3.10.x

python3 -m pip install git+https://github.com/bmschmidt/quadfeather

quadfeather-test-data 1_000_000

quadfeather --files tmp.csv --tile_size 50_000 --destination tiles

```



Then setup this library to run. It will start a local dev server.



```sh

npm i

npm run dev

```



If you go to `localhost:3344`, you should see an interactive scatterplot. To dig into what you're seeing, open `index.html`.

(In 2021, this development site works in Chrome, not Safari or Firefox, because it uses ES6 module syntax inside the webworker. The distributed version of

the module should work in all browsers.)



## Your own data.



1. Create a CSV, parquet, or feather file that has columns called 'x' and 'y'. (Or a feather file that has columns `x`, `y`). Any other columns (categorical information, etc.) can be included as additional columns.



2. Tile it:



```sh

cd deepscatter # if you're not already there

quadfeather --files ../some-path-to/your-data.csv --tile_size 50000 --destination tiles

```



3. Assuming your dataset has an `x` and `y` column and the `tiles` folder is in the root directory of this project, you can see the data visualized by running



```sh

npm run dev

```



and opening `http://localhost:3345/index-simplest-way-to-start.html` in your browser.



To edit the visualization, or to troubleshoot, look at the file `index-simplest-way-to-start.html`, where you should find a bare-bones implementation of deepscatter.



Explore `index.html`, and render it at `http://localhost:3345/index.html`, for a more advanced example.



Note: Ideally, in a future release you'll be able to create these specs in away that doesn't require coding JSON directly.



## Build the module



```sh

npm run build

```



will create an ES module at `dist/deepscatter.es.js` The mechanics of

importing this are very slightly different than `index.html`.



Note that this is an ESM module and so requires you to use `` in your code.

Don't worry! We're allowed to

do this now! But do be aware that this will not work on computers running very old browsers.



Snippet:



```html

<div id="my-div"></div>

<script type="module">

  import Scatterplot from './dist/deepscatter.umd.js';

  f = new Scatterplot('#my-div');



```



See `index_prod.html` for an example



This is currently bundled with vite and rollup. There is/will be a further interaction layer on

top of it, but the core plotting components are separate and should work as a standalone layer that supports

plot requests via an API.



# Code strategy



Any interaction logic that changes the API call directly does not belong in this library. The only

interaction code here is for zooming and interacting with points.



## Future codebase splits.



The plotting components and the tiling components are logically quite separate; I may break

the tiling strategy into a separate JS library called 'quadfeather'.



Apache Arrow would still be a necessary intermediate format, but it could be generated from CSV files

using, say, `arquero` or a WASM port of `DuckDB`.



# API



This is still subject to change and is not fully documented. The encoding portion of the API mimics Vega-Lite with some minor distinctions to avoid deeply-nested queries and to add animation and jitter parameters.



```js

{

   encoding: {

     "x": {

         "field": "x",

         "transform": "literal"

     },

     "color": {

         "field": "year",

         "range": "viridis",

         "domain": [1970, 2020]

   }

}



```



## Implemented aesthetics.



1. x

2. y

3. size

4. jitter_radius: size of jitter. API subject to change.

5. jitter_speed: speed of jitter. API subject to change.

6. color (categorical or linear: range can call color scales explicitly, or accepting any d3-color name.)

7. `x0` (for animations; transitions between x0 and x)

8. `y0` (for animations; transitions between y0 and y)

9. `filter`. (Filtering is treated as an aesthetic operation by this library.)



## Planned



1. Symbol (Mapping of categorical variables to single unicode points in a single font; probably 255 max.)

2. Label (Full-text label)

3. Image (Like PixPlot)



## Jitter



Jitter is a little overloaded with features right now, but some are quite fun.



jitter method is set on 'method' key of the 'jitter_radius' field. Possible values are:



1. `circle`

2. `spiral`

3. `time`

4. `normal`



# Principles



1. This is a 2d library. No fake 3d.

2. The central zoom state is handled by d3-zoom.

3. Use the zoom state to render other layers on top of Deepscatter by hooking in (note `on_zoom` is directly set, _not_ passed in via `prefs`):



```js

const scatterplot = new Scatterplot('#deepscatter');

scatterplot.on_zoom = (transform) => {...}

```