# Jaxtyping Summary

Use this reference when the task involves NumPy, JAX, PyTorch, TensorFlow, MLX, or array-like inputs that should carry shape and dtype information.

## Core syntax

- Use `DType[array_type, "shape"]`.
- Examples:
  - `Float[np.ndarray, "batch channels"]`
  - `Int[np.ndarray, "persons"]`
  - `Shaped[ArrayLike, "batch time features"]`
  - `Float[Tensor, "... channels"]`

## Shape rules

- Reuse names to enforce equality across values: `"batch time"` with `"time features"`.
- Use fixed integers for exact sizes: `"3 3"`.
- Use `...` for zero or more anonymous axes.
- Use `*name` for a named variadic axis.
- Use `#name` when size `1` should also be accepted for broadcasting.
- Use `_` or `name=...` only for documentation when runtime enforcement is not wanted.

## Array type guidance

- Prefer concrete normalized types in core logic:
  - `Float[np.ndarray, "..."]`
  - `Float[torch.Tensor, "..."]`
  - `Float[jax.Array, "..."]`
- Use `Shaped[ArrayLike, "..."]` or another broader input type only at ingestion boundaries.
- Create aliases for repeated shapes instead of rewriting them in every signature.

```python
from jaxtyping import Float
import numpy as np

FramePoints = Float[np.ndarray, "frames keypoints dims"]
```

## Runtime checking

- Pair `jaxtyping` with `beartype` for runtime validation:

```python
from beartype import beartype
from jaxtyping import Float, jaxtyped
import numpy as np


@jaxtyped(typechecker=beartype)
def center(x: Float[np.ndarray, "batch dims"]) -> Float[np.ndarray, "batch dims"]:
    return x - x.mean(axis=0)
```

- Apply this at stable boundaries and in tests, not blindly on every hot loop.
- Avoid `from __future__ import annotations` when relying on runtime checking.

## Practical defaults

- Prefer meaningful axis names like `batch`, `frames`, `persons`, `keypoints`, `dims`, `channels`.
- Keep aliases near the module or domain where they are used.
- If static typing and runtime truth diverge, validate at runtime first, then use a commented `cast(...)` at the narrow boundary.