# Elodin Simulation

> Create and modify physics simulations using the Elodin Python SDK. Use when writing or editing simulation Python files, defining components or systems, spawning entities, configuring 6DOF physics, setting up visualization or assets (GLB, schematics, skyboxes, terrains), or integrating with SITL/HITL workflows.

- Skill: `elodin-sys/elodin-simulation` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add elodin-sys/elodin-simulation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/elodin-sys/elodin-simulation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: elodin-sys (https://skillmd.com/u/elodin-sys)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/elodin-sys/elodin-simulation

---


# Elodin Simulation

Elodin is a JAX-based simulation platform for aerospace and physical systems. Simulations are Python scripts that define a `World`, spawn entities with components, compose systems, and run.

## Installation

```bash
pip install -U elodin          # Released SDK
elodin editor sim.py           # Run with 3D visualization
elodin run sim.py              # Headless execution
python sim.py bench --profile  # Performance profiling
```

## Simulation Structure

Every simulation follows this pattern:

```python
import elodin as el
import jax.numpy as jnp

# 1. Create world
w = el.World()

# 2. Spawn entities with archetypes
w.spawn(el.Body(
    world_pos=el.SpatialTransform(linear=jnp.array([0.0, 0.0, 10.0])),
    inertia=el.SpatialInertia(mass=1.0),
), name="ball")

# 3. Define systems
@el.map
def gravity(f: el.Force, inertia: el.Inertia) -> el.Force:
    return f + el.SpatialForce(linear=inertia.mass() * jnp.array([0.0, 0.0, -9.81]))

# 4. Compose and run
sys = el.six_dof(sys=gravity, integrator=el.Integrator.Rk4)
w.run(sys, simulation_rate=120.0)
```

## Core Concepts

### Components

Data containers defined with `typing.Annotated` + `el.Component`:

```python
import typing as ty

Wind = ty.Annotated[
    jax.Array,
    el.Component("wind", el.ComponentType(el.PrimitiveType.F64, (3,)),
                 metadata={"element_names": "x,y,z"}),
]
```

Built-in spatial types (`WorldPos`, `WorldVel`, `Force`, `Inertia`, `WorldAccel`) already carry component metadata — no `ComponentType` needed.

### Archetypes

Group components into spawnable bundles:

```python
@el.dataclass
class Satellite(el.Archetype):
    world_pos: el.WorldPos
    world_vel: el.WorldVel
    inertia: el.Inertia
    reaction_wheels: ReactionWheelCmd
```

`el.Body` is the built-in archetype providing `WorldPos`, `WorldVel`, `Inertia`, `Force`, `WorldAccel`.

### Systems

Three decorator levels — choose the simplest that fits:

| Decorator | Use when | Graph queries? |
|-----------|----------|----------------|
| `@el.map` | Simple per-entity transform, vectorized | No |
| `@el.map_seq` | Need `jax.lax.cond` short-circuit behavior | No |
| `@el.system` | Need `Query.map`, `GraphQuery.edge_fold`, or multi-query | Yes |

```python
@el.map
def drag(vel: el.WorldVel) -> el.Force:
    return el.SpatialForce(linear=-0.01 * vel.linear())

@el.system
def gravity(graph: el.GraphQuery[GravityEdge],
            q: el.Query[el.WorldPos, el.Inertia]) -> el.Query[el.Force]:
    return graph.edge_fold(q, q, el.Force, el.SpatialForce(), compute_gravity)
```

### System Composition

Chain systems with the pipe operator — order matters:

```python
sys = sensors | kalman_filter | control | el.six_dof(sys=effectors)
```

### 6DOF Physics

`el.six_dof()` integrates forces/torques into position and velocity:

```python
el.six_dof(
    sys=effectors,                    # Systems computing el.Force
    integrator=el.Integrator.Rk4,     # or Integrator.SemiImplicit
    time_step=1/300.0,                # Optional: override simulation step
)
```

**Inertia is body-frame; all other quantities are world-frame.**

### Graph Queries

Model relationships (gravity, constraints, springs) between entities:

```python
GravityEdge = ty.Annotated[el.Edge, el.Component("gravity_edge")]

w.spawn(el.Archetype(edge=GravityEdge(el.Edge(body_a_id, body_b_id))), name="a_to_b")

@el.system
def gravity(graph: el.GraphQuery[GravityEdge],
            q: el.Query[el.WorldPos, el.Inertia]) -> el.Query[el.Force]:
    return graph.edge_fold(
        left_query=q, right_query=q,
        return_type=el.Force, init_value=el.SpatialForce(),
        fold_fn=lambda acc, pos_a, m_a, pos_b, m_b: acc + compute(pos_a, m_a, pos_b, m_b),
    )
```

## Spatial Vector Algebra

Elodin uses Featherstone spatial vectors. Key types:

| Type | Shape | Represents |
|------|-------|------------|
| `SpatialTransform` | (7,) | Quaternion (4) + position (3) |
| `SpatialMotion` | (6,) | Angular vel (3) + linear vel (3) |
| `SpatialForce` | (6,) | Torque (3) + force (3) |
| `SpatialInertia` | (7,) | Inertia diagonal (3) + mass (1) + padding (3) |

Quaternion operations: `Quaternion.from_axis_angle()`, `q @ vector` (rotate), `q.inverse()`, `q.integrate_body(omega)`.

## Visualization

### Asset root (`assets/`)

Simulations load meshes, schematics, skyboxes, themes, and terrains from a single **asset root**. Paths in KDL (`glb path=…`, `icon path=…`) are relative to that root.

**Where it lives (resolution order):**

1. `$ELODIN_ASSETS` — explicit override (absolute or cwd-relative)
2. `<sim_entry_dir>/assets` — next to `main.py` / the sim entrypoint
3. `<cwd>/assets`
4. Nearest ancestor of the sim entry that contains an `assets/` directory

Repo examples usually run from the Elodin checkout root and use the shared tree at `assets/` (many `.glb` files live at the root of that tree). For a standalone sim, put an `assets/` folder beside `main.py`, or set `ELODIN_ASSETS`.

When the sim records with `db_path=…` or `ELODIN_DB_PATH`, that tree is **ingested once** into `{db}/assets/` and KDL local paths become `db:…` for portable replay. See the elodin-db skill and [DB Asset Server](https://docs.elodin.systems/reference/db-asset-server/).

### Native folders under the asset root

Only create the subtrees you need. Keys preserve subdirectories; there is no hard schema beyond these conventions:

```text
assets/
  # GLB meshes — flat at root (repo default) or under meshes/ / models/
  f22.glb
  meshes/rocket.glb          # also fine; reference as path="meshes/rocket.glb"
  models/jet.glb             # docs/tests often use models/

  schematics/                # KDL panels / window sub-schematics
    main.kdl                 # conventional active schematic key after ingest
    telemetry.kdl            # window path="telemetry.kdl" → stored under schematics/

  skyboxes/
    manifest.ron             # required for named skybox name="…"
    desert_night.cubemap.ktx2

  color_schemes/             # optional UI themes (not copied into DB by name alone)
    default_dark.json
    default_light.json

  terrains/
    planar/<region>/         # world_mesh region="<region>"
      region.toml            # optional; built-in presets exist
      config.tc              # preprocess output
      data/height/<tile>.bin
      data/albedo/<tile>.bin
    spherical/               # globe-style terrain
```

| Folder | Purpose | Referenced by |
|--------|---------|---------------|
| (root) / `meshes/` / `models/` | GLB / custom `.png` icons | `glb path="…"`, `icon path="…"` |
| `schematics/` | KDL schematics & window subtrees | Active key `schematics/main.kdl`; `window path="…"` |
| `skyboxes/` | Cubemap skyboxes | `skybox name="…"` + `manifest.ron` |
| `color_schemes/` | Editor theme JSON | `theme scheme=…` (local only; built-ins need no files) |
| `terrains/` | Planar / spherical `world_mesh` atlases | `world_mesh "death_valley"` etc. |

**Agent tips:**
- Prefer paths relative to the asset root (`path="edu-450-v2-drone.glb"`), not filesystem paths outside it.
- Do not invent reserved keys: `.elodin-ingested` and `__index__/` are DB-internal.
- Procedural meshes (`sphere`, `box`, …) and `icon builtin=…` need no asset files.
- Custom color-scheme JSON is **not** persisted into the DB today — only the scheme name travels in KDL.
- If the editor cannot find a mesh, check cwd / `ELODIN_ASSETS` before changing KDL.

### KDL Schematics

Define 3D objects and camera views in KDL files or inline:

```kdl
object_3d ball.world_pos {
    sphere radius=0.2 { color 25 50 255 }
}
object_3d aircraft.world_pos {
    glb path="f22.glb" scale=0.01 rotate="(0, 90, 0)"
}
viewport name=Chase pos="drone.world_pos.translate(-5, -5, 3)" look_at="drone.world_pos"
```

### Panel Layout (Python API)

```python
cam = el.Panel.viewport(track_entity=sat_id, fov=45.0, hdr=True, name="3D")
graph = el.Panel.graph(el.GraphEntity(sat_id, *el.Component.index(el.WorldPos)[:4]), name="Position")
w.spawn(el.Panel.vsplit(cam, graph), name="main_view")
```

## SITL/HITL Integration

The reference SITL example is `examples/betaflight-sitl/` (Betaflight flight controller in lockstep over UDP, recorded to a portable DB whose `assets/` tree makes it replayable anywhere).

Use `pre_step`/`post_step` callbacks with `StepContext` for lockstep synchronization:

```python
def post_step(tick: int, ctx: el.StepContext):
    data = ctx.component_batch_operation(reads=["drone.accel", "drone.gyro"])
    motors = flight_controller.step(accel=data["drone.accel"], gyro=data["drone.gyro"])
    ctx.write_component("drone.motor_command", motors)

w.run(sys, simulation_rate=1000.0, post_step=post_step, db_path="sitl_data")
```

Mark components as externally controlled to prevent simulation overwrite:

```python
ThrustCmd = ty.Annotated[jax.Array,
    el.Component("thrust_cmd", el.ComponentType.F64, metadata={"external_control": "true"})]
```

## Shared Constants And Monte Carlo

The Cranelift backend transparently interns baked StableHLO constants over 1 MB. For large immutable lookup tables, aero maps, ephemerides, terrain grids, or other constants, capture the JAX array in the system closure:

```python
aero_table = jnp.asarray(aero_grid)

@el.map
def aero_force(v: el.WorldVel, f: el.Force) -> el.Force:
    coeff = aero_table[0, 0]
    return f + el.SpatialForce(linear=-coeff * v.linear())
```

During StableHLO parsing, large `dense<"0x...">` blobs are moved into the content-addressed mmap cache. Multiple processes compiling the same constant map the same cache file, so Monte Carlo campaigns avoid paying N copies of the table in resident memory.

For campaign structure and memory reporting, use the native campaign runner:

```bash
elodin monte-carlo run examples/monte-carlo/main.py \
  --campaign examples/monte-carlo/campaign.toml \
  --spec examples/monte-carlo/spec.toml \
  --out dbs/monte-carlo-demo
```

Declare tunable parameters with `el.monte_carlo.params_spec(...)`, read the
current row with `el.monte_carlo.params(...)`, and optionally emit scalar outputs
with `el.monte_carlo.result(...)`. Each run writes a separate DB path. The
runner pins `ELODIN_CACHE_DIR` for all workers and auto-sizes workers/runtime
threads from available CPUs when unset. For SITL campaigns, register external
controllers with `world.recipe(...)`; the campaign runner injects worker-slot
ports into every process via `ELODIN_MONTE_CARLO_*` environment variables.
Campaign startup reaps pre-existing `elodin` and `elodin-db` processes by
default to avoid stale editor/database sessions colliding with worker ports;
use `--keep-existing` only when intentionally managing those processes yourself.
Per-run stdout/stderr lands in `runs/<run_id>/logs/`, and the runner injects
`ELODIN_SIM_SUMMARY_JSON` so each run writes a structured timing snapshot. At
campaign end, the native runner prints and writes `campaign_summary.txt` with
an aggregated version of the standard `elodin simulation summary` block plus
CPU/RAM/disk rollups. Use `--memory-probe` only for shared-constant PSS proof
runs; it enables expensive `/proc/<pid>/smaps` sampling and writes
`memory.json`/`processes.csv`.

## Execution Modes

| Mode | Command | Backend | Use |
|------|---------|---------|-----|
| Editor (GUI) | `elodin editor sim.py` | cranelift (default) | Development with 3D visualization |
| Headless | `elodin run sim.py` | cranelift (default) | CI/CD, batch processing |
| JAX backend | `w.run(sys, backend="jax-cpu")` | JAX | When cranelift doesn't support certain JAX ops |
| GPU backend | `w.run(sys, backend="jax-gpu")` | JAX GPU | Large parallel workloads |
| JAX-only | `w.to_jax(sys)` | JAX | RL training, `jax.vmap` batching |
| Compiled | `w.build(sys)` | cranelift (default) | Maximum performance |
| Real-time | `w.run(sys, simulation_rate=120.0, generate_real_time=True)` | cranelift (default) | Match wall-clock time |
| DB-connected | `w.run(sys, db_addr="0.0.0.0:2240")` | cranelift (default) | External clients + Editor |

**Backend selection:** The `backend` parameter defaults to `"cranelift"` — a pure-Rust StableHLO JIT that runs the entire tick as a single native function call, with no Python in the hot loop. Use `"jax-gpu"` for high-parallelism workloads that benefit from GPU execution. For tiny worlds, the CPU `cranelift` backend is usually fastest because kernel launch and device-transfer overhead dominates compute.

Use `examples/n-body/main.py` as the canonical benchmark. It runs the supported backends (`cranelift`, `jax-cpu`, `jax-gpu`) side-by-side:

```bash
nix develop --command ELODIN_BACKEND=jax-gpu elodin run examples/n-body/main.py
```

To compare backends, run the same command with `ELODIN_BACKEND` set to each of:
`cranelift`, `jax-cpu`, `jax-gpu`.

## Earth Gravity Models

```python
from elodin.j2 import J2              # Simple oblate Earth
from elodin.egm08 import EGM08        # High-fidelity spherical harmonics

model = EGM08(max_degree=64)          # <2.5ms at degree <=250
force = model.compute_field(x, y, z, mass)
```

## Physics Regression Testing

When changes to the simulation pipeline (Noxpr graph, cranelift-mlir compilation, shape
handling, etc.) might alter numeric output, use a database-export diff to detect
regressions.  The process:

### 1. Capture a baseline on main

```bash
git stash && git checkout main
nix develop
just install

# Run the sim, writing to a dedicated DB path
BALL_DB_PATH=dbs/ball-main uv run examples/ball/main.py bench --ticks 2000

# Export to flat CSVs (--flatten splits vector columns)
elodin-db export --format csv --flatten --output exports/ball-main dbs/ball-main
```

The `BALL_DB_PATH` env var is read by `examples/ball/main.py` and passed to
`world().run(..., db_path=...)`.  Other examples can be wired the same way.

### 2. Capture the branch under test

```bash
git checkout <branch> && git stash pop
nix develop
just install
BALL_DB_PATH=dbs/ball-branch uv run examples/ball/main.py bench --ticks 2000
elodin-db export --format csv --flatten --output exports/ball-branch dbs/ball-branch
```

### 3. Diff component-by-component (ignoring timestamps)

```bash
# Quick pass/fail for every physics component:
for f in ball.world_pos.csv ball.world_vel.csv ball.force.csv ball.wind.csv ball.world_accel.csv; do
    echo -n "$f: "
    diff <(cut -d',' -f2- exports/ball-main/$f) \
         <(cut -d',' -f2- exports/ball-branch/$f) | wc -l
done
```

Zero diff lines = bit-for-bit identical physics.  Non-zero tells you which
component diverged.  Inspect the first differing row to find the tick where
divergence starts and whether it is a large discrete jump (logic bug) or
gradual drift (floating-point).

### 4. Interpret results

| Pattern | Likely cause |
|---------|-------------|
| All zeros | Physics preserved -- safe to land |
| One component diverges at tick 1 | Compilation or shape bug -- the compiled VMFB computes something different |
| Gradual drift accumulating over ticks | Floating-point evaluation order changed (e.g., different StableHLO structure) |
| Only `wind` diverges | Random-key generation changed (seed dtype, PRNG semantics) |

### Tips

- Use `--ticks 2000` (not 1200) to expose drifts that accumulate.
- Run all three canonical benchmarks to cover every code path:
  - `ball` -- single-entity (batch1 path), uses `el.Seed` (U64) + `random.key`
  - `drone` -- multi-entity, uses `sensor_tick` (U64)
  - `cube-sat` -- JAX backend (`backend="jax-cpu"`), covers the non-IREE path
- If you need to dump the StableHLO MLIR for comparison, set
  `ELODIN_IREE_DUMP_DIR=/tmp/debug` before running.
- Clean up temp databases after: `rm -rf dbs/ball-main dbs/ball-branch exports/`.

## Additional Resources

- For the full Python API reference, see [api-reference.md](api-reference.md)
- For simulation code patterns from examples, see [examples.md](examples.md)
- Asset / DB Asset Server docs: https://docs.elodin.systems/reference/db-asset-server/
- Online docs: https://docs.elodin.systems