Procedural star rendering with three.js and WebGL shaders

Over the past few months I’ve been working on a WebGL visualization of earth’s solar neighborhood — that is, a 3D map of all stars within 75 light years of Earth, rendering stars and (exo)planets as accurately as possible.  In the process I’ve had to learn a lot about WebGL (specifically three.js, the WebGL library I’ve used).  This post goes into more detail about how I ended up doing procedural star rendering using three.js.  

The first iteration of this project rendered stars as large balls, with colors roughly mapped to star temperature.  The balls did technically tell you where a star was, but it’s not a particularly compelling visual:


Pretty much any interesting WebGL or OpenGL animation uses vertex and fragment shaders to render complex details on surfaces.  In some cases this just means mapping a fixed image onto a shape, but shaders can also be generated randomly, to represent flames, explosions, waves etc.  three.js makes it easy to attach custom vertex and fragment shaders to your meshes, so I decided to take a shot at semi-realistic (or at least, cool-looking) star rendering with my own shaders.  

Some googling brought me to a very helpful guide on the Seeds of Andromeda dev blog which outlined how to procedurally render stars using OpenGL.  This post outlines how I translated a portion of this guide to three.js, along with a few tweaks.

The full code for the fragment and vertex shaders are on GitHub.  I have images here, but the visuals are most interesting on the actual tool ( since they are larger and animated.

Usual disclaimer — I don’t know anything about astronomy, and I’m new to WebGL, so don’t assume that anything here is “correct” or implemented “cleanly”.  Feedback and suggestions welcome.

My goal was to render something along the lines of this false-color image of the sun:


In the final shader I implemented:

  • the star’s temperature is mapped to an RGB color
  • noise functions try to emulate the real texture
    • a base noise function to generate granules
    • a targeted negative noise function to generate sunspots
    • a broader noise function to generate hotter areas
  • a separate corona is added to show the star at long distances

Temperature mapping
The color of a star is determined by its temperature, following the black body radiation, color spectrum:


(sourced from wikipedia)

Since we want to render stars at the correct temperature, it makes sense to access this gradient in the shader where we are choosing  colors for pixels.  Unfortunately, WebGL limits the size of uniforms to a couple hundred on most hardware, making it tough to pack this data into the shader.

In theory WebGL implements vertex texture mapping, which would let the shader fetch the RGB coordinates from a loaded texture, but I wasn’t sure how to do this in WebGL.  So instead I broke the black-body radiation color vector into a large, horrifying, stepwise function:

  bool rbucket1 = i < 60.0; // 0, 255 in 60 bool rbucket2 = i >= 60.0 && i < 236.0;  //   255,255
  float r =
      float(rbucket1) * (0.0 + i * 4.25) +
      float(rbucket2) * (255.0) +
      float(rbucket3) * (255.0 + (i - 236.0) * -2.442) +
      float(rbucket4) * (128.0 + (i - 288.0) * -0.764) +
      float(rbucket5) * (60.0 + (i - 377.0) * -0.4477)+
      float(rbucket6) * 0.0;

Pretty disgusting.  But it works!  The full function is in the shader here

Plugging in the Sun’s temperature (5,778) gives us an exciting shade of off-white:


While beautiful, we can do better.

Base noise function (granules)

Going forward I diverge a bit from the SoA guide.  While the SoA guide chooses a temperature and then varies the intensity of the texture based on a noise function, I instead fix high and low surface temperatures for the star, and use the noise function to vary between them.  The high and low temperatures are passed into the shader as uniforms:

 var material = new THREE.ShaderMaterial({
   uniforms: {
     time: uniforms.time,
     scale: uniforms.scale,
     highTemp: {type: "f", value: starData.temperatureEstimate.value.quantity},
     lowTemp: {type: "f", value: starData.temperatureEstimate.value.quantity / 4}
   vertexShader: shaders.dynamicVertexShader,
   fragmentShader: shaders.starFragmentShader,
   transparent: false,
   polygonOffset: -.1,
   usePolygonOffset: true

All the noise functions below shift the pixel temperature, which is then mapped to an RGB color.

Convection currents on the surface of the sun generate noisy “granules” of hotter and cooler areas.  To represent these granules an available WebGL implementation of 3D simplex noise.    The base noise for a pixel is just the simplex noise at the vertex coordinates, plus some magic numbers (simply tuned to whatever looked “realistic”):

  void main( void ) {
    float noiseBase = (noise(vTexCoord3D , .40, 0.7)+1.0)/2.0;

The number of octaves in the simplex noise determines the “depth” of the noise, as zoom increases.  The tradeoff of course is that each octave increases the work the GPU computes each frame, so more octaves == fewer frames per second.  Here is the sun rendered at 2 octaves:


4 octaves (which I ended up using):


and 8 octaves (too intense to render real-time with acceptable performance):



Sunspots are areas on the surface of a star with a reduced surface temperature due to magnetic field flux.  My implementation of sunspots is pretty simple; I take the same noise function we used for the granules, but with a decreased frequency, higher amplitude and initial offset.  By only taking the positive values (the max function), the sunspots show up as discrete features rather than continuous noise.  The final value (“ss”) is then subtracted from the initial noise.

  float frequency = 0.04;
  float t1 = snoise(vTexCoord3D * frequency)*2.7 -  1.9;
  float ss = max(0.0, t1);

This adds only a single snoise call per pixel, and looks reasonably good:


Additional temperature variation

To add a bit more noise, the noise function is used one last time, this time to add temperature in broader areas, for a bit more noise:

  float brightNoise= snoise(vTexCoord3D * .02)*1.4- .9;
  float brightSpot = max(0.0, brightNoise);

  float total = noiseBase - ss + brightSpot;

All together, this is what the final shader looks like:



Stars are very small, on a stellar scale.  The main goal of this project is to be able to visually hop around the Earth’s solar neighborhood, so we need to be able to see stars at a long distance (like we can in real life).  

The easiest solution is to just have a very large fixed sprite attached at the star’s location.  This solution has some issues though:

  • being inside a large semi-opaque sprite (ex, when zoomed up towards a star) occludes vision of everything else
  • scaled sprites in Three.js do not play well with raycasting (the raycaster misses the sprite, making it impossible to select stars by mousing over them)
  • a fixed sprite will not vary its color by star temperature

I ended up implementing a shader which implemented a corona shader with

  • RGB color based on the star’s temperature (same implementation as above)
  • color near the focus trending towards pure white
  • size was proportional to camera distance (up to a max distance)
  • a bit of lens flare (this didn’t work very well)

Full code here.  Lots of magic constants for aesthetics, like before.

Close to the target star, the corona is mostly occluded by the detail mesh:


At a distance the corona remains visible:


On a cooler (temperature) star:


The corona mesh serves two purposes

  • calculating intersections during raycasting (to enable targeting stars via mouseover and clicking)
  • star visibility

Using a custom shader to implement both of these use-cases let me cut the number of rendered three.js meshes in half; this is great, because rendering half as many objects means each frame renders twice as quickly.


This shader is a pretty good first step, but I’d like to make a few improvements and additions when I have a chance:

  • Solar flares (and other 3D surface activity)
  • More accurate sunspot rendering (the size and frequency aren’t based on any real science)
  • Fix coronas to more accurately represent a star’s real visual magnitude — the most obvious ones here are the largest ones, not necessarily the brightest ones

My goal is to follow up this post a couple others about parts of this project I think turned out well, starting with the orbit controls (the logic for panning the camera around a fixed point while orbiting).  

Posted in Github, Open Source, Uncategorized, Uncharted, Visualization | Leave a comment

3D map of Solar Neighborhood using three.js (again!)

A few years ago I posted about a WebGL visualization of the neighborhood around our sun.  It was never as polished as I wanted, so on-and-off over the past few months I’ve been working on making it more interesting.  The project is still located here:

The code is still hosted on GitHub:

Two of the improvements I’m especially excited about.  First the star rendering now uses glsl shaders which are based on the star’s temperature, giving cool (and animated!) visuals:


Second, all known exoplanets (planets orbiting stars besides our Sun) are rendered around their parent stars.  The textures here are of course fake, but the orbits are accurate where the data is known:


I’ve also included all the full planets in our solar system with full textures and (hopefully accurate) orbits:


I’ve updated the README on the GitHub project with all the changes (I’ve also totally reworked the controls).

I’m going to try to write some more granular posts about what actually went into the three.js and glsl to implement this, since I learned a ton in the process.


Posted in Github, Open Source, Uncharted, Visualization | Leave a comment

Catalog of Life Taxonomic Tree

A small visualization I’ve wanted to do for a while is a tree of life graph — visualizing all known species and their relationships.

Recently I found that the Catalog of Life project has a very accessible database of all known species / taxonomic groups and their relationships, available for download here.  This let me put together a simple site backed by their database, available here:


All the source code is available on Github.


I’ve used dagre + d3 on a number of other graph visualization projects, so dagre-d3 was the natural choice for the  visualization component.  The actual code required to do the graph building and rendering is pretty trivial.

The data fetching was a bit trickier.  Since pre-loading tens of millions of records was obviously unrealistic, I had to implement a graph class (BackedBiGraph) which lazily expands and collapses, using user-provided callbacks to fetch new data.  In this case, the callbacks were just ajax calls back to the server.

The Catalog of Life database did not come with a Java client, so I thought this would be a good opportunity to use jOOQ to generate Java models and query builders corresponding to the COL database, since I am allergic to writing actual SQL queries.  This ended up working very well — configuring the jOOQ Maven plugin was simple, and the generated code made writing the queries trivial:

 private Collection&lt;TaxonNodeInfo&gt; taxonInfo(Condition condition) {
.where(condition).fetch().stream().map(record -&gt; new TaxonNodeInfo(

All in all, there are a lot of rough edges still, but dagre, d3 and jOOQ made this a much easier project than expected.  The code is on Github, so suggestions, improvements, or bugfixes are always welcome.



Posted in Github, Open Source, Visualization | 7 Comments

D3 NLP Visualizer Update

A couple years ago I put together a simple NLP parse tree visualizer demo which used d3 and the dagre layout library.  At the time, integrating dagre with d3 required a bit of manual work (or copy-paste); however, since then dagre-d3 library was split out of dagre which added an easy API for adding and removing nodes.  Even though the library isn’t under active development, I think it’s still the most powerful pure-JS directed graph layout library out there.

An example from the wiki shows how simple the the dagre-d3 API is for creating directed graphs, abbreviated here:

    // Create the input graph
    var g = new dagreD3.graphlib.Graph()
      .setDefaultEdgeLabel(function() { return {}; });

    // Here we"re setting nodeclass, which is used by our custom drawNodes function
    // below.
    g.setNode(0,  { label: "TOP", class: "type-TOP" });

    // ... snip

    // Set up edges, no special attributes.
    g.setEdge(3, 4);
    // ... snip
    // Create the renderer
    var render = new dagreD3.render();

    var svg ="svg"),
        svgGroup = svg.append("g");

    // Run the renderer. This is what draws the final graph.
    render("svg g"), g);

Since that original demo site still gets a fair amount of traffic, I thought it would be good to update it to use dagre-d3 instead of the original hand-rolled bindings (along with other cleanup).  You can see the change set required here.

The other goal was to re-familiarize myself with d3 and dagre, since I have a couple projects in mind which would make heavy use of both.  Hopefully I’ll have something to post here in the next couple months.

Posted in Github, Open Source, Visualization | Leave a comment

3d Map of our Solar Neighborhood using three.js

A few months ago I stumbled on three.js, a library which exposes a simple WebGL interface.  I was really impressed at both the performance of WebGL and how easy three.js made building high-performance animations in the browser.

I thought this would be a good opportunity to put together a visualization I’ve been looking for for a while–a map of our solar neighborhood, showing all the closest stars to our own.  The data-set I used was the HYG database of nearby stars, which is a compilation of all stars within 50 parsecs.  I’ve cross-referenced stars against wikipedia where available.   The site is available here:



The whole project is open-source, hosted here.

The project isn’t nearly as polished as I would have liked (there’s a long to-do list on the github page), but I’m trying to commit to releasing projects rather than letting them die silently.  I’m hoping to be able to iterate on the remaining issues over the next few months.

Thoughts, suggestions, or contributions always welcome here or on the GitHub page.

Posted in Uncategorized | 1 Comment

Simple Boolean Expression Manipulation in Java

I’ve worked on a couple projects recently where I needed to be able to do some lightweight propositional expression manipulation in Java.  Specifically, I wanted to be able to:

  • Let a user input simple logical expressions, and parse them into Java data structures
  • Evaluate the truth of the statement given values for each variable
  • Incrementally update the expression as values are assigned to the variables
  • If the statement given some variable assignments is not definitively true or false, show which terms remain.
  • Perform basic simplification of redundant terms (full satisfiability is of course NP hard, so this would only include basic simplification)

I couldn’t find a Java library which made this particularly easy; a couple stackoverflow questions I found didn’t have any particularly easy solutions.  I decided to take a shot at implementing a basic library.  The result is on GitHub as the jbool_expressions library.

(most of the rest of this is copied from the README, so feel free to read it there.)

Using the library, a basic propositional expression is built out of the types And, Or, Not, Variable and Literal. All of these extend the base type Expression.  An Expression can be built programatically:

    Expression expr = And.of(
        Or.of(Variable.of("C"), Not.of(Variable.of("C"))));

or by parsing a string:

    Expression expr =
        ExprParser.parse("( ( (! C) | C) & A & B)");

The expression is the same either way:

    ((!C | C) & A & B)

We can do some basic simplification to eliminate the redundant terms:

    Expression simplified = RuleSet.simplify(expr);

to see the redundant terms are simplified to “true”:

    (A & B)

We can assign a value to one of the variables, and see that the expression is simplified after assigning “A” a value:

    Expression halfAssigned = RuleSet.assign(
        Collections.singletonMap("A", true)

We can see the remaining expression:


If we assign a value to the remaining variable, we can see the expression evaluate to a literal:

    Expression resolved = RuleSet.assign(
         Collections.singletonMap("B", true)

All expressions are immutable (we got a new expression back each time we performed an operation), so we can see that the original expression is unmodified:

    ((!C | C) & A & B)

Expressions can also be converted to sum-of-products form:

    Expression nonStandard = PrefixParser.parse(
        "(* (+ A B) (+ C D))"


    Expression sopForm = RuleSet.toSop(nonStandard);
    ((A | B) & (C | D))
    ((A & C) | (A & D) | (B & C) | (B & D))

You can build the library yourself or grab it via maven:


Happy to hear any feedback / bugs / improvements etc. I’d also be interested in hearing how other people have dealt with this problem, and if there are any better libraries out there.

Posted in Algorithms, Github, Open Source | Leave a comment

Taxi Loading at SFO

I usually avoid catching Taxis whenever possible, but when I arrived in SFO last week the trains were no longer running and I hadn’t arranged for a shuttle, so I ended up waiting in line to catch a Taxi.  The line was structured something like this:

Taxi line 1 (1)

  • There was a loading area about four cars long where Taxis were loading passengers
  • Would-be passengers waited in line along the curb to the left, waiting for a Taxi
  • Likewise, taxis waited in line for passengers on the other side of the curb
  • As people loaded into Taxis and departed, each line advanced to the right, matching the front of the Taxi line with the front of the passenger line
  • An airport employee stood stood near the front of the line, shepherding people and cabs around to enforce this flow

Of course, this felt like an extremely inefficient system; I was waiting next to a cab which was waiting for a passenger; had we been allowed, I would have just jumped in the cab next to me and we both would have been happier.  However, since the line of people was denser than the Taxi line, I would have been cutting in front of other people in line.

In college I took a couple classes where we learned about queuing algorithms and the standard trade-offs involved.  On the ride back I thought about how they applied to the Taxi-loading situation here:

  • Throughput: how many passengers per hour could the system match to Taxis?   This was not being optimized for, or I could have gotten into the Taxi beside me.
  • Fairness: this was pretty clearly what was being optimized for–both the Taxi line and the passenger line were being processed in First-In-First-Out (FIFO) ordering. 
  • Average wait time:  I don’t think wait time was being taken into account, especially since passengers with less luggage (and therefore faster loading) would have been given priority over passengers with many bags.

A couple other issues were specific to this situation:

  • The matching process should not involve an inordinate amount of walking by prospective passengers (a passenger should never have to walk the entire length of the Taxi queue to find a cab)
  • If cabs frequently have to pass other cabs to advance to the head of the queue, it increases the odds of an accident (or of getting run over, if you are loading your bags into the trunk.)

I’d like to think that a better system exists (“there has to be a better way!”), even if it sacrifices some amount of fairness, since clearly this system would scale poorly if the airport was busier.

If anyone knows of airports/malls/etc that do a better job, I’d be interested in knowing how they manage it.  I didn’t waste an enormous amount of time in line (~10 minutes), but if the line is on average 50 people long, that’s actually a huge amount of time being squandered over the course of a year.

Posted in Algorithms | 1 Comment