unxt-hypothesis 1.8.2

Hypothesis strategies for unxt

unxt-hypothesis

Hypothesis strategies for property-based testing with unxt.

This package provides Hypothesis strategies for generating random Quantity, Unit, and UnitSystem objects for property-based testing.

Quick Start

from hypothesis import given

import unxt as u
import unxt_hypothesis as ust


@given(q=ust.quantities(unit="km/s"))
def test_quantity_property(q):
    """Test that all quantities have value and unit."""
    assert q.value is not None
    assert q.unit is not None


@given(u=ust.units("length"))
def test_unit_property(u):
    """Test that units can be converted to strings."""
    assert str(u) is not None


@given(sys=ust.unitsystems("m", "s", "kg", "rad"))
def test_unitsystem_property(sys):
    """Test that unit systems have expected base units."""
    assert len(sys) == 4

Strategies

derived_units(base, *, integer_powers=True, max_complexity=3)

Generate units that are dimensionally equivalent to a given base unit.

Parameters:

• base (str | apyu.UnitBase | SearchStrategy): Base unit (e.g., "m", "s", "kg") or a hypothesis strategy that generates such units.
• integer_powers (bool): If True, only generate units with integer powers of base units (default: True).
• max_complexity (int): Maximum number of additional base unit factors to combine (default: 3).

Returns: unxt.AbstractUnit

units(dimension=None, *, max_complexity=2, allow_non_integer_powers=False)

Generate random Unit objects from astropy.

Parameters:

• dimension (str | apyu.PhysicalType | None): The physical dimension of the unit. If None, generates units from various dimensions. Examples: "length", "velocity", "energy".
• max_complexity (int): Maximum complexity of compound units (default: 2).
• allow_non_integer_powers (bool): Whether to allow non-integer powers in units (default: False).

Returns: unxt.AbstractUnit

quantities(*, shape=None, dtype=None, unit=None)

Generate random Quantity objects.

Parameters:

• shape (int | tuple[int, ...] | st.SearchStrategy | None): Shape of the array. Can be:
  • None (default): Generates small arrays with various shapes
  • int: Scalar shape specification (e.g., 3 for shape (3,))
  • tuple: Explicit shape (e.g., (3, 3) for a 3×3 matrix)
  • Strategy: A Hypothesis strategy that generates shapes
• dtype (np.dtype | st.SearchStrategy | None): Data type of the array. Defaults to float32.
• unit (str | apyu.UnitBase | st.SearchStrategy | None): Unit for the quantity. Can be:
  • None (default): Generates quantities with various common units
  • str: Specific unit string (e.g., "m", "km/s")
  • apyu.UnitBase: Specific unit object
  • Strategy: A Hypothesis strategy that generates units (e.g., from units())

Returns: unxt.Quantity

unitsystems(*units)

Generate random UnitSystem objects.

Parameters:

• *units (str | apyu.UnitBase | st.SearchStrategy[apyu.UnitBase]): Variable number of unit specifications. Each can be:
  • str: Fixed unit string (e.g., "m", "kg")
  • apyu.UnitBase: Fixed unit object
  • Strategy: A Hypothesis strategy that generates units

Returns: unxt.AbstractUnitSystem

Type Strategy Registration

The package automatically registers type strategies for Hypothesis's st.from_type() function, enabling automatic strategy generation for unxt types:

from hypothesis import given, strategies as st

import unxt as u
import unxt_hypothesis as ust  # Import to register strategies


# Hypothesis automatically uses the registered strategies
@given(q=st.from_type(u.AbstractQuantity))
def test_quantity_via_from_type(q):
    """Test quantities generated via st.from_type()."""
    assert isinstance(q, u.AbstractQuantity)
    assert u.dimension_of(q) is not None


@given(a=st.from_type(u.Angle))
def test_angle_via_from_type(a):
    """Test angles generated via st.from_type()."""
    assert isinstance(a, u.Angle)
    assert u.dimension_of(a) == u.dimension("angle")


@given(usys=st.from_type(u.AbstractUnitSystem))
def test_unitsystem_via_from_type(usys):
    """Test unit systems generated via st.from_type()."""
    assert isinstance(usys, u.AbstractUnitSystem)

This integration allows you to use type annotations directly in your tests without explicitly importing the strategy functions, making tests more concise and easier to read.

Examples

Generate quantities with specific shapes

from hypothesis import given, strategies as st

import unxt_hypothesis as ust


@given(q=ust.quantities(shape=(3, 3)))
def test_matrix_quantity(q):
    assert q.shape == (3, 3)


@given(q=ust.quantities(shape=()))
def test_scalar_quantity(q):
    assert q.ndim == 0

Generate quantities with specific dimensions

from hypothesis import given

import unxt as u
import unxt_hypothesis as ust


@given(q=ust.quantities(unit=ust.units("length")))
def test_length_quantity(q):
    assert u.dimension_of(q) == u.dimension("length")


@given(q=ust.quantities(unit=ust.units("energy")))
def test_energy_quantity(q):
    assert u.dimension_of(q) == u.dimension("energy")

Testing Unitful Functions

Here's a complete example of using these strategies to test a physics function:

import jax.numpy as jnp
from hypothesis import given

import unxt as u
import unxt_hypothesis as ust


def kinetic_energy(mass, velocity):
    """Calculate kinetic energy: KE = 0.5 * m * v^2"""
    return 0.5 * mass * velocity**2


@given(
    mass=ust.quantities(unit="kg", shape=()),
    velocity=ust.quantities(unit="m/s", shape=()),
)
def test_kinetic_energy_positive(mass, velocity):
    """Kinetic energy is always non-negative."""
    ke = kinetic_energy(mass, velocity)
    assert jnp.all(ke.value >= 0)
    # Check resulting unit is energy
    assert u.dimension_of(ke) == u.dimension("energy")


@given(
    mass=ust.quantities(unit="kg", shape=(10,)),
    velocity=ust.quantities(unit="m/s", shape=(10,)),
)
def test_kinetic_energy_vectorized(mass, velocity):
    """Kinetic energy works with arrays."""
    ke = kinetic_energy(mass, velocity)
    assert ke.shape == (10,)
    assert jnp.all(ke.value >= 0)

Combining Strategies

The strategies are designed to work together seamlessly:

from hypothesis import given, strategies as st

import unxt as u
import unxt_hypothesis as ust


# Create quantities with units from a unit strategy
@given(unit=ust.units("length"), q=ust.quantities(unit=ust.units("length")))
def test_consistent_length_units(unit, q):
    """Both unit and q have length dimension."""
    assert u.dimension_of(unit) == u.dimension("length")
    assert u.dimension_of(q) == u.dimension("length")


# Create unit systems with varying complexity
@given(
    sys=ust.unitsystems(
        ust.units("length", max_complexity=1),
        ust.units("time", max_complexity=1),
        ust.units("mass", max_complexity=1),
        "rad",
    )
)
def test_simple_unit_system(sys):
    """Generate systems with simple base units only."""
    assert len(sys) == 4

Documentation

For full documentation and advanced examples, see:

• unxt-hypothesis Documentation
• Hypothesis Documentation
• unxt Documentation

Contributing

Contributions are welcome! Please see the main unxt repository for contributing guidelines.

Documentation

For comprehensive documentation, examples, and guides, see the unxt documentation.

License

BSD 3-Clause License. See LICENSE for details.

Contributing

Contributions are welcome! Please see the main unxt repository for contributing guidelines.