Made a (half-broken and ugly) twisty torus
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README.md
33
README.md
@ -2,16 +2,31 @@
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## Highest priority:
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- Clean up `ramhorn_branch` because it's fugly.
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- Adaptive subdivision - which means having to generalize past some
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`vmap` stuff.
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- Try some non-deterministic examples
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- Get identical or near-identical meshes to `ramhorn_branch` from
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Python. (Should just be a matter of tweaking parameters.)
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- See `automata_scratch/examples.py` and implement some of the tougher
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examples.
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- `spiral_nested_2` & `spiral_nested_3` (how to compose
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efficiently?)
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- `twisty_torus`
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- `twisty_torus`, `spiral_nested_2`, & `spiral_nested_3` are all
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that remain. To do them, I need to compose transformations (not
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in the matrix sense), but I also probably need to produce
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RuleEvals which always have `xf` of identity transformation since
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the Python code does not 'inherit' transforms unless I tell it to.
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## Important but less critical:
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- Elegance & succinctness (my recent closure work may help with this):
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- Look at performance.
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- Start at `to_mesh_iter()`. The cost of small appends/connects
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seems to be killing performance.
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- `connect()` is a big performance hot-spot: 85% of total time in
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one test, around 51% in `extend()`, 33% in `clone()`. It seems
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like I should be able to share geometry with the `Rc` (like noted
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above), defer copying until actually needed, and pre-allocate the
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vector to its size (which should be easy to compute).
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- Elegance & succinctness:
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- Clean up `ramhorn_branch` because it's ugly.
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- What patterns can I factor out? I do some things regularly, like:
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the clockwise boundaries, the zigzag connections.
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- Declarative macro to shorten this `Tag::Parent`, `Tag::Body`
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@ -22,14 +37,6 @@
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things like my patterns with closures (e.g. the Y combinator like
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method for recursive calls).
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- Docs on modules
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- Look at performance.
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- Start at `to_mesh_iter()`. The cost of small appends/connects
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seems to be killing performance.
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- `connect()` is a big performance hot-spot: 85% of total time in
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one test, around 51% in `extend()`, 33% in `clone()`. It seems
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like I should be able to share geometry with the `Rc` (like noted
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above), defer copying until actually needed, and pre-allocate the
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vector to its size (which should be easy to compute).
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- Compute global scale factor, and perhaps pass it to a rule (to
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eventually be used for, perhaps, adaptive subdivision)
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- swept-isocontour stuff from
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@ -44,7 +44,8 @@ pub fn cube_thing() -> Rule<()> {
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Rule { eval: Rc::new(rec), ctxt: () }
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}
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// Meant to be a copy of twist_from_gen from Python & automata_scratch
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// Meant to be a copy of twist_from_gen from Python &
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// automata_scratch, but has since acquired a sort of life of its own
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pub fn twist(f: f32, subdiv: usize) -> Rule<()> {
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// TODO: Clean this code up. It was a very naive conversion from
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// the non-closure version.
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@ -71,6 +72,9 @@ pub fn twist(f: f32, subdiv: usize) -> Rule<()> {
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let seed2 = seed.clone();
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// TODO: Why do I need the above?
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// TODO: Could a macro get rid of some of this or would it just be
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// equally cumbersome because I'd have to sort of pass 'seed'
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// explicitly?
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let recur = move |incr: Transform| -> RuleFn<()> {
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let seed_next = incr.transform(&seed2);
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@ -133,6 +137,96 @@ pub fn twist(f: f32, subdiv: usize) -> Rule<()> {
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Rule { eval: Rc::new(start), ctxt: () }
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}
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#[derive(Copy, Clone)]
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pub struct TorusCtxt {
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xform1: Transform,
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xform2: Transform,
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}
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pub fn twisty_torus() -> Rule<TorusCtxt> {
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let subdiv = 8;
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let seed = vec![
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vertex(-0.5, -0.5, 1.0),
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vertex(-0.5, 0.5, 1.0),
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vertex( 0.5, 0.5, 1.0),
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vertex( 0.5, -0.5, 1.0),
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];
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let seed = util::subdivide_cycle(&seed, subdiv);
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let n = seed.len();
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let geom = Rc::new(util::zigzag_to_parent(seed.clone(), n));
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let (vc, faces) = util::connect_convex(&seed, true);
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let final_geom = Rc::new(OpenMesh {
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verts: vec![vc],
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faces: faces,
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});
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let rad = 4.0;
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let dx0 = 2.0;
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let ang = 0.1;
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let recur = move |self_: Rc<Rule<TorusCtxt>>| -> RuleEval<TorusCtxt> {
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let y = &Vector3::y_axis();
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let z = &Vector3::z_axis();
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let xf1 = self_.ctxt.xform1;
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let xf2 = self_.ctxt.xform2;
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let next_rule = Rule {
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eval: self_.eval.clone(),
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ctxt: TorusCtxt {
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xform1: xf1.rotate(y, 0.1),
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xform2: xf2.rotate(z, ang),
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},
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};
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let xf = xf1 * xf2;
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RuleEval {
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geom: Rc::new(geom.transform(&xf)),
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final_geom: Rc::new(final_geom.transform(&xf)),
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children: vec![
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Child {
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rule: Rc::new(next_rule),
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xf: Transform::new(),
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vmap: (0..n).collect(),
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},
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],
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}
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};
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let start = move |self_: Rc<Rule<TorusCtxt>>| -> RuleEval<TorusCtxt> {
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let xf1 = self_.ctxt.xform1;
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let xf2 = self_.ctxt.xform2;
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let xf = xf1 * xf2;
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let mut s2 = seed.clone();
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let (centroid, f) = util::connect_convex(&s2, false);
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s2.push(centroid);
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let n2 = s2.len();
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let g = OpenMesh { verts: s2, faces: f };
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let fg = prim::empty_mesh();
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RuleEval {
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geom: Rc::new(g.transform(&xf)),
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final_geom: Rc::new(fg),
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children: vec![
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Child {
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rule: Rc::new(Rule {
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eval: Rc::new(recur.clone()),
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ctxt: self_.ctxt,
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}),
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xf: Transform::new(),
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vmap: (0..n2).collect(),
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},
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],
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}
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};
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Rule {
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eval: Rc::new(start),
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ctxt: TorusCtxt {
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xform1: Transform::new().translate(rad, 0.0, 0.0),
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xform2: Transform::new().translate(dx0, 0.0, 0.0),
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},
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}
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}
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pub fn ramhorn() -> Rule<()> {
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let v = Unit::new_normalize(Vector3::new(-1.0, 0.0, 1.0));
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@ -50,6 +50,11 @@ mod tests {
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run_test(examples::twist(1.0, 2), 200, "screw", false);
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}
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#[test]
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fn twisty_torus() {
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run_test(examples::twisty_torus(), 50, "twisty_torus", false);
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}
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// This one is very time-consuming to run:
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#[test]
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#[ignore]
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