deeptrack.types Module#

Type declarations for internal use.

This module defines type aliases and utility types to standardize the type annotations used throughout the codebase. It enhances code readability, maintainability, and reduces redundancy in type annotations. These types are particularly useful for properties and array-like structures used within the library.

Defined Types#

  • PropertyLike

    A type alias representing a value of type T or a callable returning T.

  • ArrayLike

    A type alias for array-like structures (e.g., tuples, lists, numpy arrays).

  • NumberLike

    A type alias for numeric types, including scalars and arrays (e.g., numpy arrays, GPU tensors).

Examples#

Using PropertyLike:

>>> def scale(value: PropertyLike[float]) -> float:
...     if callable(value):
...         return value()
...     return value
>>> scale(3.14)  # 3.14
>>> scale(lambda: 2.71)  # 2.71

Using ArrayLike:

>>> import numpy as np
>>> def compute_mean(array: ArrayLike[float]) -> float:
...     return np.mean(array)
>>> compute_mean([1.0, 2.0, 3.0])  # 2.0
>>> compute_mean((4.0, 5.0, 6.0))  # 5.0
>>> compute_mean(np.array([7.0, 8.0, 9.0]))  # 8.0

Using NumberLike:

>>> def add_numbers(a: NumberLike, b: NumberLike) -> NumberLike:
...     return a + b
>>> add_numbers(5, 3.2)  # 8.2
>>> add_numbers(np.array([1, 2, 3]), 4)  # array([5, 6, 7])
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