microecs 0.3.6

MicroECS: Minimal Entity Component System (ECS) in python and numpy

MicroECS

Minimal (~300 LoC) Entity Component System in python and numpy. Examples also use raylib for rendering.

Usage:

• Via pip: pip install microecs
• From source code:

git clone https://gitlab.com/meehai/microecs      # clone the source code
cd microecs                                       # go in the cloned directory
python -m venv .venv && source .venv/bin/activate # make a virtual env, optional but useful
python -m pip install -e .                        # install micro ecs in this virtual env
python -m pytest test/                            # run the unit & integration tests to verify installation
python examples/01-hello-world.py                 # run the basic hello world example (others in that dir)

Docs: meehai.gitlab.io/microecs

Relevant primitives: Component, Pool, QueryResult, World

There are only 4 primitives (bottom up):

• Component is a simple python dataclass holding only data. All entries must be numpy arrays with metadata fields: shape and dtype. We support 5 dtypes only: int32, float32, bool, str and object. A component with no fields is a valid tag for querying (e.g. class Frozen(Component): pass).
• Pool is a simple 'archetype' dynamic array, holding entities of the same type (same set of components). Usses Components metadata to construct contiguous arrays for all entities of the same type.
• QueryResult is a list of pools that match some query on all the entities of the World. It acts as a contiguous numpy-like container that implements numpy's interface. For all intents and purposes it should feel like a (N, ...) view over all selected entities. To get a proper numpy array, use qr.numpy(). To iterate over each entity in a query result (e.g. rendering), use for eid, position in zip(qr.entity_ids, qr.position): ....
• World is a manager of Pools and has an overview of all the entities in the scene. It also manages the migration of entities from one pool to the other. A World can also require extra metadata keys on every field via World(extra_metadata=["serializable"]), to enforce component-level behavior such as field serialization.

Few relevant concepts:

• Pool operates on array indices, while World operates on entity IDs (also integers). This allows seamless movement between pools while the high-level systems still working as intended.
• All mutable operations on World are lazy. These are: add_entity, remove_entity, add_component, remove_component. They are added to a command buffer which is only executed when calling world.update().
• Systems are a convention, they are not part of this library. They can be defined at application level and act as hooks or callbacks. The World object doesn't need to know more than entities and components.

Super simplified main loop structure

from typing import Callable
import numpy as np
import raylib as rl
from microecs import World, Component

# components
class HasPosition(Component):
    # 'shape' + 'dtype' are always required. For additional metadata (e.g. examples/03-serialization) use extra_metadata
    position: np.ndarray = field(metadata={"shape": (2, ), "dtype": "float32"})
class HasVelocity(Component):
    velocity: np.ndarray = field(metadata={"shape": (2, ), "dtype": "float32"})
class HasColor(Component):
    color: np.ndarray = field(metadata={"shape": (4, ), "dtype": "int32"})

# systems: Note they are a convention!
class RenderSystem:
    def __call__(self, world: World):
        query_result = world.query(HasPosition, HasColor, exclude=[]) # contiguous-like view of all entities matching
        for position, color in zip(query_result.position, query_result.color): # draw each entity
            DrawEntity(position, color)

class MotionSystem:
    def __call__(self, world: World):
        qr = world.query(HasPosition, HasVelocity) # 'exclude' is optional
        qr.position[:] = qr.position + qr.velocity * DT # writes back to all the underlying pools using numpy's rules
        # Alternative for per-pool update. Less ergonomic, but maybe faster in extreme cases as it avoids the _Field obj
        for pool in qr.pool_list:
            pool.position[:] = pool.position + pool.velocity * DT

def main():
    render_system: list[Callable] = RenderSystem()
    update_systems: list[Callable] = [MotionSystem()]

    world = World(components=[HasPosition, HasColor, HasVelocity], extra_metadata=None) # extra_metadata is optional
    for _ in range(n_objects):
        # NOTE: world.{add/remove}_{entity/component} are lazy. They take effect after the first world.update() call.
        world.add_entity(components=(HasPosition, HasVelocity, HasColor), # tuple of components (types)
                         position=           np.array((x, y), "float32"), # data as kwargs
                         color=         np.array("black", dtype="int32"),
                         velocity=         np.array((vx, vy), "float32"))

    while not rl.WindowShouldClose():
        world.update() # must be called at each tick so the lazy methods are processed and entities are updated
        # update stuff...
        _ = [system(world=world) for system in update_systems]
        # draw stuff, e.g. using raylib
        rl.BeginDrawing()
        rl.ClearBackground(rl.RAYWHITE)
        rl.DrawFPS(rl.GetScreenWidth() - 100, 0)
        render_system(world=world)
        rl.EndDrawing()

Per-entity systems (e.g. rendering, foreign APIs)

Not every system vectorizes. A renderer calls a draw function per primitive; same for any per-entity foreign API. That's a per-entity system inside an otherwise-vectorized app. The cheapest way to iterate through all the entities is by using zip() on the QueryResult object on the fields you need, e.g:

qr = world.query(HasPosition, HasColor, HasRadius)
for pos, color, radius in zip(qr.position, qr.color, qr.radius):     # one entity per step, fields aligned
    rl.DrawCircle(int(pos[0]), int(pos[1]), float(radius[0]), color) # per-entity by necessity (no "draw all")

There are other ways to extract a single entity e.g. qr.some_field[i] or e = world.get_entity(eid), but these are slower in the general case and should be avoided in expensive loops, like rendering, even if they are a bit more ergonomic. See benchmark below.

Microbenchmark: ECS vs OOP on a simple physics step

Benchmark: we run the same physics step pos += vel*dt over N=100k entities split across 2 pools in various ways (ECS or OOP). All methods are verified to produce the identical result. Reproduce with python test/manual/perf/bench_ecs_vs_oop.py (it prints {mode: avg_seconds_per_step}).

pattern	ns/entity	vs OOP-scalar
micro-ecs-pool-vectorized — for pool: pool.f[:] = pool.f + …	0.9	52× faster
micro-ecs-vectorized — qr.f[:] = qr.f + … (the _Field)	1.8	27× faster
oop-scalar — for o: o.x += o.vx*dt (python floats)	48	1× (baseline)
oop-numpy — objects holding (2,) numpy arrays	605	13× slower
micro-ecs-zip-rows — for p, v in zip(qr.pos, qr.vel)	744	15× slower
micro-ecs-pool-loop — for pool: for i: pool.f[i]	870	18× slower
micro-ecs-get-entity — world.get_entity(eid) per entity	1674	35× slower
micro-ecs-index — qr.f[i] per entity (an np.searchsorted each)	4573	95× slower

Three things to take from it:

1. Vectorized wins big. Batched ops (_Field or per-pool) run at 1–2 ns/entity — 27–52× faster than the fastest OOP loop. Same for data-parallel branches: an np.where clamp or bounce is ~34× faster than a per-entity if.
2. Per-entity loops are a cliff, not a tie. Every per-entity microecs path is 15–95× slower than idiomatic float-based OOP — because microecs is numpy-backed, so a per-entity step pays numpy's tiny-array overhead (oop-numpy shows the same ~13× tax). One unavoidable per-entity pass (~750 ns/entity) costs ~500× a vectorized op (~1.5 ns) and will dominate the frame.
3. If you must loop, loop right. zip-rows (15×) < pool-loop (18×) < get_entity (35×) < qr.f[i] (95× — never in a hot loop; qr.f[i] += … also raises, you must bind the row first).

Rule of thumb: keep systems vectorized and push branches into np.where / np.clip. If a workload is irreducibly per-entity (data-dependent control flow), plain python objects beat microecs ~15× — use them there. microecs is the right tool for vectorizable simulation.

How much are Pool and QueryResult numpy-like and corner cases

Given qr=world.query(A, B), then qr.position returns a _Field: a view over the matching pools that behaves like one contiguous-like (N, *e) numpy array (e.g. (N, 2) for a (2,) field). It applies each op per pool and stitches result back.

That covers elementwise math and ufuncs (e.g. np.where, np.linalg.norm(..., axis=1) etc.), broadcasting (every operand shape numpy accepts, and it raises on the ones numpy rejects). See test/unit/test_field_numpy_parity.py for a whole set of operations comparing both.

Edge cases worth knowing:

• Not a full ndarray — these raise, never lie. Entity-axis indexing beyond a single qr.f[i] (qr.f[:], qr.f[2:4], qr.f[mask], fancy), partial entity writes, and ndarray methods/attrs (.sum(), .mean(), .dtype, .ndim, .T). Need any of these? Materialize first with qr.f.numpy().
• Axis-0 ops are per-pool, not global (footgun). np.sort / np.cumsum / np.sum over axis=0 run within each pool and reset at pool boundaries — they do not see all entities at once, so they differ from numpy. They're allowed, but if you want a global result, do qr.f.numpy() first. A reduction that collapses the entity axis is rejected when its length no longer matches the pool's row count.
• Operands must come from the same query. Alignment is per-pool, not by flat index, so don't mix a _Field from one world.query(...) into an op on another.