unxt API

Construct a unxt.AbstractQuantity from an array-like value and a unit.

Parameters:

value – The array-like value.
unit – The unit of the value.
dtype – The data type of the array (keyword-only).
args (Any)
kwargs (Any)

Return type:

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> u.Q.from_(x, "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_([1.0, 2, 3], "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_((1.0, 2, 3), "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

Make a unxt.AbstractQuantity from an array-like value and a unit kwarg.

Examples

For this example we’ll use the unxt.Quantity class. The same applies to any subclass of unxt.AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_([1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], *, value: Any, unit: Any, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a AbstractQuantity from value and unit kwargs.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_(value=[1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], mapping: Mapping[str, Any]) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from a Mapping.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> q = u.Q.from_({"value": x, "unit": "m"})
>>> q
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_({"value": q, "unit": "km"})
Quantity(Array([0.001, 0.002, 0.003], dtype=float32), unit='km')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: Any, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, "cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: NoneType, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, None)
Quantity(Array(1, dtype=int32...), unit='m')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, /, *, unit: Any | None = None, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The unit is unchanged.

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a StaticQuantity, keeping the value on NumPy dtypes.

The generic AbstractQuantity.from_ routes the value through jnp.asarray, which applies JAX’s x64-disabled dtype rules and silently downcasts int64 / float64 to int32 / float32. A StaticQuantity stores its value verbatim, so hand the value straight to __init__ and let the StaticValue.from_ converter convert it – that preserves the NumPy dtype. Delegating (rather than calling np.asarray here) also keeps .from_ and __init__ under the same policy for JAX inputs, instead of materialising an array the constructor would reject. (The keyword-unit overload delegates here, so it is covered too.)

Examples

>>> import numpy as np
>>> import unxt as u

>>> u.StaticQuantity.from_(
...     np.array([1, 2, 3], dtype=np.int64), "m"
... ).value.array.dtype
dtype('int64')

.from_ applies the same JAX-input policy as __init__ – it delegates rather than converting first, so the two cannot drift:

>>> import jax.numpy as jnp
>>> u.StaticQuantity.from_(jnp.array([1.0, 2.0]), "m")
StaticQuantity(array([1., 2.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"))
Quantity(Array(1., dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, u: Any, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity, converting to a target unit.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"), "cm")
Quantity(Array(100., dtype=float32), unit='cm')

Parameters:

cls (type[AbstractQuantity])
args (Any)
kwargs (Any)

Return type:

uconvert(u: Any, /)

Convert the quantity to the given units.

See also

None: convert a quantity to a new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.uconvert("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

ustrip(u: Any, /)

Return the value in the given units.

See also

None: strip the units from a quantity.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.ustrip("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Array

property shape: tuple[int, ...]: Shape of the array.

property dtype: dtype

Data type of the array.

Examples

>>> import unxt as u
>>> u.Q(1, "m").dtype
dtype('int32')

property device: Device

Device where the array is located.

Examples

>>> import unxt as u
>>> u.Q(1, "m").device
CpuDevice(id=0)

property mT: AbstractQuantity

Matrix transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.mT
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

property ndim: int

Number of dimensions.

Examples

>>> import unxt as u
>>> q = u.Q([[1]], "m")
>>> q.ndim
2

property size: int

Total number of elements.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.size
3

property T: AbstractQuantity

Transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.T
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

to_device(device: None | Device = None)

Move the array to a new device.

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.to_device(None)
Quantity(Array(1, dtype=int32...), unit='m')

Parameters:: device (None | Device)
Return type:: AbstractQuantity

argmax(*args: Any, **kwargs: Any)

Return the indices of the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmax()
Array(2, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

argmin(*args: Any, **kwargs: Any)

Return the indices of the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmin()
Array(0, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

astype(*args: Any, **kwargs: Any)

Copy the array and cast to a specified dtype.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.dtype
dtype('int32')

>>> q.astype(float)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property at: _QuantityIndexUpdateHelper

Helper property for index update functionality.

The at property provides a functionally pure equivalent of in-place array modifications.

In particular:

Alternate syntax	Equivalent In-place expression
`x = x.at[idx].set(y)`	`x[idx] = y`
`x = x.at[idx].add(y)`	`x[idx] += y`
`x = x.at[idx].subtract(y)`	`x[idx] -= y`
`x = x.at[idx].multiply(y)`	`x[idx] *= y`
`x = x.at[idx].divide(y)`	`x[idx] /= y`
`x = x.at[idx].power(y)`	`x[idx] **= y`
`x = x.at[idx].min(y)`	`x[idx] = minimum(x[idx], y)`
`x = x.at[idx].max(y)`	`x[idx] = maximum(x[idx], y)`
`x = x.at[idx].apply(ufunc)`	`ufunc.at(x, idx)`
`x = x.at[idx].get()`	`x = x[idx]`

None of the x.at expressions modify the original x; instead they return a modified copy of x. However, inside a jit() compiled function, expressions like x = x.at[idx].set(y) are guaranteed to be applied in-place.

Unlike NumPy in-place operations such as x[idx] += y, if multiple indices refer to the same location, all updates will be applied (NumPy would only apply the last update, rather than applying all updates.) The order in which conflicting updates are applied is implementation-defined and may be nondeterministic (e.g., due to concurrency on some hardware platforms).

By default, JAX assumes that all indices are in-bounds. Alternative out-of-bound index semantics can be specified via the mode parameter (see below).

Parameters:

mode –
string specifying out-of-bound indexing mode. Options are:
- "promise_in_bounds": (default) The user promises that indices are in bounds. No additional checking will be performed. In practice, this means that out-of-bounds indices in get() will be clipped, and out-of-bounds indices in set(), add(), etc. will be dropped.
- "clip": clamp out of bounds indices into valid range.
- "drop": ignore out-of-bound indices.
- "fill": alias for "drop". For get(), the optional fill_value argument specifies the value that will be returned.
See jax.lax.GatherScatterMode for more details.
wrap_negative_indices – If True (default) then negative indices indicate position from the end of the array, similar to Python and NumPy indexing. If False, then negative indices are considered out-of-bounds and behave according to the mode parameter.
fill_value – Only applies to the get() method: the fill value to return for out-of-bounds slices when mode is 'fill'. Ignored otherwise. Defaults to NaN for inexact types, the largest negative value for signed types, the largest positive value for unsigned types, and True for booleans.
indices_are_sorted – If True, the implementation will assume that the (normalized) indices passed to at[] are sorted in ascending order, which can lead to more efficient execution on some backends. If True but the indices are not actually sorted, the output is undefined.
unique_indices – If True, the implementation will assume that the (normalized) indices passed to at[] are unique, which can result in more efficient execution on some backends. If True but the indices are not actually unique, the output is undefined.

Examples

>>> x = jnp.arange(5.0)
>>> x
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[2].get()
Array(2., dtype=float32)
>>> x.at[2].add(10)
Array([ 0.,  1., 12.,  3.,  4.], dtype=float32)

By default, out-of-bound indices are ignored in updates, but this behavior can be controlled with the mode parameter:

>>> x.at[10].add(10)  # dropped
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[20].add(10, mode='clip')  # clipped
Array([ 0.,  1.,  2.,  3., 14.], dtype=float32)

For get(), out-of-bound indices are clipped by default:

>>> x.at[20].get()  # out-of-bounds indices clipped
Array(4., dtype=float32)
>>> x.at[20].get(mode='fill')  # out-of-bounds indices filled with NaN
Array(nan, dtype=float32)
>>> x.at[20].get(mode='fill', fill_value=-1)  # custom fill value
Array(-1., dtype=float32)

Negative indices count from the end of the array, but this behavior can be disabled by setting wrap_negative_indices = False:

>>> x.at[-1].set(99)
Array([ 0.,  1.,  2.,  3., 99.], dtype=float32)
>>> x.at[-1].set(99, wrap_negative_indices=False, mode='drop')  # dropped!
Array([0., 1., 2., 3., 4.], dtype=float32)

block_until_ready()

Block until the array is ready.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.block_until_ready() is q
True

devices()

Return the devices where the array is located.

Return type:: set[Device]

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.devices()
{CpuDevice(id=0)}

flatten()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.flatten()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

max(*args: Any, **kwargs: Any)

Return the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.max()
Quantity(Array(3, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

mean(*args: Any, **kwargs: Any)

Return the mean value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.mean()
Quantity(Array(2., dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

min(*args: Any, **kwargs: Any)

Return the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.min()
Quantity(Array(1, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

ravel()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.ravel()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

reshape(*args: Any, order: str = 'C')

Return a reshaped version of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3, 4], "m")
>>> q.reshape(2, 2)
Quantity(Array([[1, 2],
                          [3, 4]], dtype=int32), unit='m')

Parameters:

args (Any)
order (str)

Return type:

round(*args: Any, **kwargs: Any)

Round the array to the given number of decimals.

Examples

>>> import unxt as u
>>> q = u.Q([1.1, 2.2, 3.3], "m")
>>> q.round(0)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property sharding: Any

Return the sharding configuration of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.sharding
SingleDeviceSharding(device=..., memory_kind=...)

squeeze(*args: Any, **kwargs: Any)

Return the array with all single-dimensional entries removed.

Examples

>>> import unxt as u
>>> q = u.Q([[[1], [2], [3]]], "m")
>>> q.squeeze()
Quantity(Array([1, 2, 3], dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

is_equivalent(other: AbstractQuantity, /)

Whether self and other are physically equal (unit-aware).

The method form of unxt.equivalent; unlike == (which is unit-blind for StaticValue-backed quantities) this accounts for unit conversion.

Examples

>>> import unxt as u
>>> u.Q(1000.0, "m").is_equivalent(u.Q(1.0, "km"))
Quantity(Array(True, dtype=bool...), unit='')

Parameters:: other (AbstractQuantity)
Return type:: Any

decompose(bases: Sequence[UnitBase | FunctionUnitBase | str], /)

Decompose the quantity into the given bases.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.decompose(["cm", "s"])
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: bases (Sequence[UnitBase | FunctionUnitBase | str])
Return type:: AbstractQuantity

to(u: Any, /)

Convert the quantity to the given units.

See unxt.quantity.AbstractQuantity.uconvert.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

to_value(u: Any, /)

Return the value in the given units.

See unxt.AbstractQuantity.ustrip.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to_value("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Union[Array, ndarray, bool, number, bool, int, float, complex]

final class unxt.Angle(value: Any, unit: Any)

Bases: AbstractAngle

Angular quantity.

Examples

>>> import unxt as u

Create an Angle:

>>> q = u.Angle(1, "rad")
>>> q
Angle(Array(1, dtype=int32...), unit='rad')

Wrap an Angle to a range:

>>> q = u.Angle(370, "deg")
>>> q.wrap_to(u.Q(0, "deg"), u.Q(360, "deg"))
Angle(Array(10, dtype=int32...), unit='deg')

Create an Angle array:

>>> q = u.Angle([1, 2, 3], "deg")
>>> q
Angle(Array([1, 2, 3], dtype=int32), unit='deg')

Do math on an Angle:

>>> 2 * q
Angle(Array([2, 4, 6], dtype=int32), unit='deg')

>>> q % u.Q(4, "deg")
Angle(Array([1, 2, 3], dtype=int32), unit='deg')

Parameters:

value (Any)
unit (Any)

value: Real[Array, '*shape'] | Real[StaticValue, '*shape']: The value of the unxt.AbstractQuantity.

property T: AbstractQuantity

Transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.T
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

argmax(*args: Any, **kwargs: Any)

Return the indices of the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmax()
Array(2, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

argmin(*args: Any, **kwargs: Any)

Return the indices of the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmin()
Array(0, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

astype(*args: Any, **kwargs: Any)

Copy the array and cast to a specified dtype.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.dtype
dtype('int32')

>>> q.astype(float)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property at: _QuantityIndexUpdateHelper

Helper property for index update functionality.

The at property provides a functionally pure equivalent of in-place array modifications.

In particular:

Alternate syntax	Equivalent In-place expression
`x = x.at[idx].set(y)`	`x[idx] = y`
`x = x.at[idx].add(y)`	`x[idx] += y`
`x = x.at[idx].subtract(y)`	`x[idx] -= y`
`x = x.at[idx].multiply(y)`	`x[idx] *= y`
`x = x.at[idx].divide(y)`	`x[idx] /= y`
`x = x.at[idx].power(y)`	`x[idx] **= y`
`x = x.at[idx].min(y)`	`x[idx] = minimum(x[idx], y)`
`x = x.at[idx].max(y)`	`x[idx] = maximum(x[idx], y)`
`x = x.at[idx].apply(ufunc)`	`ufunc.at(x, idx)`
`x = x.at[idx].get()`	`x = x[idx]`

None of the x.at expressions modify the original x; instead they return a modified copy of x. However, inside a jit() compiled function, expressions like x = x.at[idx].set(y) are guaranteed to be applied in-place.

Unlike NumPy in-place operations such as x[idx] += y, if multiple indices refer to the same location, all updates will be applied (NumPy would only apply the last update, rather than applying all updates.) The order in which conflicting updates are applied is implementation-defined and may be nondeterministic (e.g., due to concurrency on some hardware platforms).

By default, JAX assumes that all indices are in-bounds. Alternative out-of-bound index semantics can be specified via the mode parameter (see below).

Parameters:

mode –
string specifying out-of-bound indexing mode. Options are:
- "promise_in_bounds": (default) The user promises that indices are in bounds. No additional checking will be performed. In practice, this means that out-of-bounds indices in get() will be clipped, and out-of-bounds indices in set(), add(), etc. will be dropped.
- "clip": clamp out of bounds indices into valid range.
- "drop": ignore out-of-bound indices.
- "fill": alias for "drop". For get(), the optional fill_value argument specifies the value that will be returned.
See jax.lax.GatherScatterMode for more details.
wrap_negative_indices – If True (default) then negative indices indicate position from the end of the array, similar to Python and NumPy indexing. If False, then negative indices are considered out-of-bounds and behave according to the mode parameter.
fill_value – Only applies to the get() method: the fill value to return for out-of-bounds slices when mode is 'fill'. Ignored otherwise. Defaults to NaN for inexact types, the largest negative value for signed types, the largest positive value for unsigned types, and True for booleans.
indices_are_sorted – If True, the implementation will assume that the (normalized) indices passed to at[] are sorted in ascending order, which can lead to more efficient execution on some backends. If True but the indices are not actually sorted, the output is undefined.
unique_indices – If True, the implementation will assume that the (normalized) indices passed to at[] are unique, which can result in more efficient execution on some backends. If True but the indices are not actually unique, the output is undefined.

Examples

>>> x = jnp.arange(5.0)
>>> x
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[2].get()
Array(2., dtype=float32)
>>> x.at[2].add(10)
Array([ 0.,  1., 12.,  3.,  4.], dtype=float32)

By default, out-of-bound indices are ignored in updates, but this behavior can be controlled with the mode parameter:

>>> x.at[10].add(10)  # dropped
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[20].add(10, mode='clip')  # clipped
Array([ 0.,  1.,  2.,  3., 14.], dtype=float32)

For get(), out-of-bound indices are clipped by default:

>>> x.at[20].get()  # out-of-bounds indices clipped
Array(4., dtype=float32)
>>> x.at[20].get(mode='fill')  # out-of-bounds indices filled with NaN
Array(nan, dtype=float32)
>>> x.at[20].get(mode='fill', fill_value=-1)  # custom fill value
Array(-1., dtype=float32)

Negative indices count from the end of the array, but this behavior can be disabled by setting wrap_negative_indices = False:

>>> x.at[-1].set(99)
Array([ 0.,  1.,  2.,  3., 99.], dtype=float32)
>>> x.at[-1].set(99, wrap_negative_indices=False, mode='drop')  # dropped!
Array([0., 1., 2., 3., 4.], dtype=float32)

block_until_ready()

Block until the array is ready.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.block_until_ready() is q
True

decompose(bases: Sequence[UnitBase | FunctionUnitBase | str], /)

Decompose the quantity into the given bases.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.decompose(["cm", "s"])
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: bases (Sequence[UnitBase | FunctionUnitBase | str])
Return type:: AbstractQuantity

property device: Device

Device where the array is located.

Examples

>>> import unxt as u
>>> u.Q(1, "m").device
CpuDevice(id=0)

devices()

Return the devices where the array is located.

Return type:: set[Device]

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.devices()
{CpuDevice(id=0)}

property dtype: dtype

Data type of the array.

Examples

>>> import unxt as u
>>> u.Q(1, "m").dtype
dtype('int32')

flatten()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.flatten()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

classmethod from_(cls: type[AbstractQuantity], *args: Any, **kwargs: Any)

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a unxt.AbstractQuantity from an array-like value and a unit.

Parameters:

value – The array-like value.
unit – The unit of the value.
dtype – The data type of the array (keyword-only).
args (Any)
kwargs (Any)

Return type:

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> u.Q.from_(x, "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_([1.0, 2, 3], "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_((1.0, 2, 3), "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

Make a unxt.AbstractQuantity from an array-like value and a unit kwarg.

Examples

For this example we’ll use the unxt.Quantity class. The same applies to any subclass of unxt.AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_([1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], *, value: Any, unit: Any, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a AbstractQuantity from value and unit kwargs.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_(value=[1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], mapping: Mapping[str, Any]) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from a Mapping.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> q = u.Q.from_({"value": x, "unit": "m"})
>>> q
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_({"value": q, "unit": "km"})
Quantity(Array([0.001, 0.002, 0.003], dtype=float32), unit='km')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: Any, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, "cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: NoneType, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, None)
Quantity(Array(1, dtype=int32...), unit='m')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, /, *, unit: Any | None = None, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The unit is unchanged.

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a StaticQuantity, keeping the value on NumPy dtypes.

The generic AbstractQuantity.from_ routes the value through jnp.asarray, which applies JAX’s x64-disabled dtype rules and silently downcasts int64 / float64 to int32 / float32. A StaticQuantity stores its value verbatim, so hand the value straight to __init__ and let the StaticValue.from_ converter convert it – that preserves the NumPy dtype. Delegating (rather than calling np.asarray here) also keeps .from_ and __init__ under the same policy for JAX inputs, instead of materialising an array the constructor would reject. (The keyword-unit overload delegates here, so it is covered too.)

Examples

>>> import numpy as np
>>> import unxt as u

>>> u.StaticQuantity.from_(
...     np.array([1, 2, 3], dtype=np.int64), "m"
... ).value.array.dtype
dtype('int64')

.from_ applies the same JAX-input policy as __init__ – it delegates rather than converting first, so the two cannot drift:

>>> import jax.numpy as jnp
>>> u.StaticQuantity.from_(jnp.array([1.0, 2.0]), "m")
StaticQuantity(array([1., 2.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"))
Quantity(Array(1., dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, u: Any, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity, converting to a target unit.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"), "cm")
Quantity(Array(100., dtype=float32), unit='cm')

Parameters:

cls (type[AbstractQuantity])
args (Any)
kwargs (Any)

Return type:

is_equivalent(other: AbstractQuantity, /)

Whether self and other are physically equal (unit-aware).

The method form of unxt.equivalent; unlike == (which is unit-blind for StaticValue-backed quantities) this accounts for unit conversion.

Examples

>>> import unxt as u
>>> u.Q(1000.0, "m").is_equivalent(u.Q(1.0, "km"))
Quantity(Array(True, dtype=bool...), unit='')

Parameters:: other (AbstractQuantity)
Return type:: Any

property mT: AbstractQuantity

Matrix transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.mT
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

max(*args: Any, **kwargs: Any)

Return the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.max()
Quantity(Array(3, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

mean(*args: Any, **kwargs: Any)

Return the mean value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.mean()
Quantity(Array(2., dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

min(*args: Any, **kwargs: Any)

Return the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.min()
Quantity(Array(1, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property ndim: int

Number of dimensions.

Examples

>>> import unxt as u
>>> q = u.Q([[1]], "m")
>>> q.ndim
2

ravel()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.ravel()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

reshape(*args: Any, order: str = 'C')

Return a reshaped version of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3, 4], "m")
>>> q.reshape(2, 2)
Quantity(Array([[1, 2],
                          [3, 4]], dtype=int32), unit='m')

Parameters:

args (Any)
order (str)

Return type:

round(*args: Any, **kwargs: Any)

Round the array to the given number of decimals.

Examples

>>> import unxt as u
>>> q = u.Q([1.1, 2.2, 3.3], "m")
>>> q.round(0)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property shape: tuple[int, ...]: Shape of the array.

property sharding: Any

Return the sharding configuration of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.sharding
SingleDeviceSharding(device=..., memory_kind=...)

property size: int

Total number of elements.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.size
3

squeeze(*args: Any, **kwargs: Any)

Return the array with all single-dimensional entries removed.

Examples

>>> import unxt as u
>>> q = u.Q([[[1], [2], [3]]], "m")
>>> q.squeeze()
Quantity(Array([1, 2, 3], dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

to(u: Any, /)

Convert the quantity to the given units.

See unxt.quantity.AbstractQuantity.uconvert.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

to_device(device: None | Device = None)

Move the array to a new device.

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.to_device(None)
Quantity(Array(1, dtype=int32...), unit='m')

Parameters:: device (None | Device)
Return type:: AbstractQuantity

to_value(u: Any, /)

Return the value in the given units.

See unxt.AbstractQuantity.ustrip.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to_value("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Union[Array, ndarray, bool, number, bool, int, float, complex]

uconvert(u: Any, /)

Convert the quantity to the given units.

See also

None: convert a quantity to a new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.uconvert("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

ustrip(u: Any, /)

Return the value in the given units.

See also

None: strip the units from a quantity.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.ustrip("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Array

wrap_to(min: AbstractQuantity, max: AbstractQuantity)

Wrap the angle to the range [min, max).

Parameters:

min (AbstractQuantity) – The minimum, maximum value of the range.
max (AbstractQuantity) – The minimum, maximum value of the range.

Return type:

AbstractAngle

See also

None: functional version of this method.

Return type:

AbstractAngle

Parameters:

min (AbstractQuantity)
max (AbstractQuantity)

Examples

>>> import unxt as u
>>> angle = u.Angle(370, "deg")
>>> angle.wrap_to(min=u.Q(0, "deg"), max=u.Q(360, "deg"))
Angle(Array(10, dtype=int32...), unit='deg')

unit: UnitBase | FunctionUnitBase: The unit associated with this value.

unxt.Q: alias of Quantity

class unxt.Quantity(value: Any, unit: Any)

Bases: AbstractQuantity

The default quantity: units without dimension parametrization.

This class is not parametrized by its dimensionality, making it a single class (and a single JAX pytree type) regardless of dimension. For runtime dimension checking and dimension-specific dispatch, see unxts.parametric.ParametricQuantity.

Examples

>>> import unxt as u
>>> u.Quantity(1, "m")
Quantity(Array(1, dtype=int32...), unit='m')

Parameters:

value (Any)
unit (Any)

value: Shaped[Array, '*shape'] | Shaped[StaticValue, '*shape']: The value of the Quantity.

unit: UnitBase | FunctionUnitBase: The unit associated with this value.

short_name: ClassVar[str] = 'Q'

property T: AbstractQuantity

Transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.T
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

argmax(*args: Any, **kwargs: Any)

Return the indices of the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmax()
Array(2, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

argmin(*args: Any, **kwargs: Any)

Return the indices of the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmin()
Array(0, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

astype(*args: Any, **kwargs: Any)

Copy the array and cast to a specified dtype.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.dtype
dtype('int32')

>>> q.astype(float)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property at: _QuantityIndexUpdateHelper

Helper property for index update functionality.

The at property provides a functionally pure equivalent of in-place array modifications.

In particular:

Alternate syntax	Equivalent In-place expression
`x = x.at[idx].set(y)`	`x[idx] = y`
`x = x.at[idx].add(y)`	`x[idx] += y`
`x = x.at[idx].subtract(y)`	`x[idx] -= y`
`x = x.at[idx].multiply(y)`	`x[idx] *= y`
`x = x.at[idx].divide(y)`	`x[idx] /= y`
`x = x.at[idx].power(y)`	`x[idx] **= y`
`x = x.at[idx].min(y)`	`x[idx] = minimum(x[idx], y)`
`x = x.at[idx].max(y)`	`x[idx] = maximum(x[idx], y)`
`x = x.at[idx].apply(ufunc)`	`ufunc.at(x, idx)`
`x = x.at[idx].get()`	`x = x[idx]`

None of the x.at expressions modify the original x; instead they return a modified copy of x. However, inside a jit() compiled function, expressions like x = x.at[idx].set(y) are guaranteed to be applied in-place.

Unlike NumPy in-place operations such as x[idx] += y, if multiple indices refer to the same location, all updates will be applied (NumPy would only apply the last update, rather than applying all updates.) The order in which conflicting updates are applied is implementation-defined and may be nondeterministic (e.g., due to concurrency on some hardware platforms).

By default, JAX assumes that all indices are in-bounds. Alternative out-of-bound index semantics can be specified via the mode parameter (see below).

Parameters:

mode –
string specifying out-of-bound indexing mode. Options are:
- "promise_in_bounds": (default) The user promises that indices are in bounds. No additional checking will be performed. In practice, this means that out-of-bounds indices in get() will be clipped, and out-of-bounds indices in set(), add(), etc. will be dropped.
- "clip": clamp out of bounds indices into valid range.
- "drop": ignore out-of-bound indices.
- "fill": alias for "drop". For get(), the optional fill_value argument specifies the value that will be returned.
See jax.lax.GatherScatterMode for more details.
wrap_negative_indices – If True (default) then negative indices indicate position from the end of the array, similar to Python and NumPy indexing. If False, then negative indices are considered out-of-bounds and behave according to the mode parameter.
fill_value – Only applies to the get() method: the fill value to return for out-of-bounds slices when mode is 'fill'. Ignored otherwise. Defaults to NaN for inexact types, the largest negative value for signed types, the largest positive value for unsigned types, and True for booleans.
indices_are_sorted – If True, the implementation will assume that the (normalized) indices passed to at[] are sorted in ascending order, which can lead to more efficient execution on some backends. If True but the indices are not actually sorted, the output is undefined.
unique_indices – If True, the implementation will assume that the (normalized) indices passed to at[] are unique, which can result in more efficient execution on some backends. If True but the indices are not actually unique, the output is undefined.

Examples

>>> x = jnp.arange(5.0)
>>> x
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[2].get()
Array(2., dtype=float32)
>>> x.at[2].add(10)
Array([ 0.,  1., 12.,  3.,  4.], dtype=float32)

By default, out-of-bound indices are ignored in updates, but this behavior can be controlled with the mode parameter:

>>> x.at[10].add(10)  # dropped
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[20].add(10, mode='clip')  # clipped
Array([ 0.,  1.,  2.,  3., 14.], dtype=float32)

For get(), out-of-bound indices are clipped by default:

>>> x.at[20].get()  # out-of-bounds indices clipped
Array(4., dtype=float32)
>>> x.at[20].get(mode='fill')  # out-of-bounds indices filled with NaN
Array(nan, dtype=float32)
>>> x.at[20].get(mode='fill', fill_value=-1)  # custom fill value
Array(-1., dtype=float32)

Negative indices count from the end of the array, but this behavior can be disabled by setting wrap_negative_indices = False:

>>> x.at[-1].set(99)
Array([ 0.,  1.,  2.,  3., 99.], dtype=float32)
>>> x.at[-1].set(99, wrap_negative_indices=False, mode='drop')  # dropped!
Array([0., 1., 2., 3., 4.], dtype=float32)

block_until_ready()

Block until the array is ready.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.block_until_ready() is q
True

decompose(bases: Sequence[UnitBase | FunctionUnitBase | str], /)

Decompose the quantity into the given bases.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.decompose(["cm", "s"])
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: bases (Sequence[UnitBase | FunctionUnitBase | str])
Return type:: AbstractQuantity

property device: Device

Device where the array is located.

Examples

>>> import unxt as u
>>> u.Q(1, "m").device
CpuDevice(id=0)

devices()

Return the devices where the array is located.

Return type:: set[Device]

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.devices()
{CpuDevice(id=0)}

property dtype: dtype

Data type of the array.

Examples

>>> import unxt as u
>>> u.Q(1, "m").dtype
dtype('int32')

flatten()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.flatten()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

classmethod from_(cls: type[AbstractQuantity], *args: Any, **kwargs: Any)

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a unxt.AbstractQuantity from an array-like value and a unit.

Parameters:

value – The array-like value.
unit – The unit of the value.
dtype – The data type of the array (keyword-only).
args (Any)
kwargs (Any)

Return type:

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> u.Q.from_(x, "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_([1.0, 2, 3], "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_((1.0, 2, 3), "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

Make a unxt.AbstractQuantity from an array-like value and a unit kwarg.

Examples

For this example we’ll use the unxt.Quantity class. The same applies to any subclass of unxt.AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_([1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], *, value: Any, unit: Any, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a AbstractQuantity from value and unit kwargs.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_(value=[1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], mapping: Mapping[str, Any]) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from a Mapping.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> q = u.Q.from_({"value": x, "unit": "m"})
>>> q
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_({"value": q, "unit": "km"})
Quantity(Array([0.001, 0.002, 0.003], dtype=float32), unit='km')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: Any, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, "cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: NoneType, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, None)
Quantity(Array(1, dtype=int32...), unit='m')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, /, *, unit: Any | None = None, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The unit is unchanged.

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a StaticQuantity, keeping the value on NumPy dtypes.

The generic AbstractQuantity.from_ routes the value through jnp.asarray, which applies JAX’s x64-disabled dtype rules and silently downcasts int64 / float64 to int32 / float32. A StaticQuantity stores its value verbatim, so hand the value straight to __init__ and let the StaticValue.from_ converter convert it – that preserves the NumPy dtype. Delegating (rather than calling np.asarray here) also keeps .from_ and __init__ under the same policy for JAX inputs, instead of materialising an array the constructor would reject. (The keyword-unit overload delegates here, so it is covered too.)

Examples

>>> import numpy as np
>>> import unxt as u

>>> u.StaticQuantity.from_(
...     np.array([1, 2, 3], dtype=np.int64), "m"
... ).value.array.dtype
dtype('int64')

.from_ applies the same JAX-input policy as __init__ – it delegates rather than converting first, so the two cannot drift:

>>> import jax.numpy as jnp
>>> u.StaticQuantity.from_(jnp.array([1.0, 2.0]), "m")
StaticQuantity(array([1., 2.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"))
Quantity(Array(1., dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, u: Any, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity, converting to a target unit.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"), "cm")
Quantity(Array(100., dtype=float32), unit='cm')

Parameters:

cls (type[AbstractQuantity])
args (Any)
kwargs (Any)

Return type:

is_equivalent(other: AbstractQuantity, /)

Whether self and other are physically equal (unit-aware).

The method form of unxt.equivalent; unlike == (which is unit-blind for StaticValue-backed quantities) this accounts for unit conversion.

Examples

>>> import unxt as u
>>> u.Q(1000.0, "m").is_equivalent(u.Q(1.0, "km"))
Quantity(Array(True, dtype=bool...), unit='')

Parameters:: other (AbstractQuantity)
Return type:: Any

property mT: AbstractQuantity

Matrix transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.mT
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

max(*args: Any, **kwargs: Any)

Return the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.max()
Quantity(Array(3, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

mean(*args: Any, **kwargs: Any)

Return the mean value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.mean()
Quantity(Array(2., dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

min(*args: Any, **kwargs: Any)

Return the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.min()
Quantity(Array(1, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property ndim: int

Number of dimensions.

Examples

>>> import unxt as u
>>> q = u.Q([[1]], "m")
>>> q.ndim
2

ravel()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.ravel()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

reshape(*args: Any, order: str = 'C')

Return a reshaped version of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3, 4], "m")
>>> q.reshape(2, 2)
Quantity(Array([[1, 2],
                          [3, 4]], dtype=int32), unit='m')

Parameters:

args (Any)
order (str)

Return type:

round(*args: Any, **kwargs: Any)

Round the array to the given number of decimals.

Examples

>>> import unxt as u
>>> q = u.Q([1.1, 2.2, 3.3], "m")
>>> q.round(0)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property shape: tuple[int, ...]: Shape of the array.

property sharding: Any

Return the sharding configuration of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.sharding
SingleDeviceSharding(device=..., memory_kind=...)

property size: int

Total number of elements.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.size
3

squeeze(*args: Any, **kwargs: Any)

Return the array with all single-dimensional entries removed.

Examples

>>> import unxt as u
>>> q = u.Q([[[1], [2], [3]]], "m")
>>> q.squeeze()
Quantity(Array([1, 2, 3], dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

to(u: Any, /)

Convert the quantity to the given units.

See unxt.quantity.AbstractQuantity.uconvert.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

to_device(device: None | Device = None)

Move the array to a new device.

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.to_device(None)
Quantity(Array(1, dtype=int32...), unit='m')

Parameters:: device (None | Device)
Return type:: AbstractQuantity

to_value(u: Any, /)

Return the value in the given units.

See unxt.AbstractQuantity.ustrip.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to_value("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Union[Array, ndarray, bool, number, bool, int, float, complex]

uconvert(u: Any, /)

Convert the quantity to the given units.

See also

None: convert a quantity to a new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.uconvert("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

ustrip(u: Any, /)

Return the value in the given units.

See also

None: strip the units from a quantity.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.ustrip("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Array

final class unxt.StaticQuantity(value: Any, unit: Any)

Bases: AbstractQuantity

A non-parametric quantity whose value is always a static NumPy array.

Unlike ~unxt.Quantity, its value is stored as a static (hashable) NumPy array, which lets a StaticQuantity be passed as a static argument to a jax.jit-compiled function. It accepts Python scalars and array-like inputs convertible to NumPy arrays; a concrete (eager) JAX array is materialised back to NumPy, and only a traced value – which cannot be static – is rejected.

Examples

>>> import numpy as np
>>> import unxt as u

Basic construction:

>>> q = u.StaticQuantity(np.array([1.0, 2.0]), "m")
>>> q
StaticQuantity(array([1., 2.]), unit='m')

Values are static and hashable:

>>> isinstance(hash(q), int)
True

A concrete JAX array is materialised back to NumPy (a static value is just data); only traced values (under jit/vmap/grad) are rejected:

>>> import jax.numpy as jnp
>>> u.StaticQuantity(jnp.array([1.0, 2.0]), "m")
StaticQuantity(array([1., 2.], dtype=float32), unit='m')

The Wadler-Lindig representation hides the internal static wrapper:

>>> import wadler_lindig as wl
>>> wl.pprint(q, short_arrays=False)
StaticQuantity(array([1., 2.]), unit='m')

Parameters:

value (Any)
unit (Any)

value: StaticValue: The static value of the AbstractQuantity.

unit: UnitBase | FunctionUnitBase: The unit associated with this value.

property T: AbstractQuantity

Transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.T
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

argmax(*args: Any, **kwargs: Any)

Return the indices of the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmax()
Array(2, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

argmin(*args: Any, **kwargs: Any)

Return the indices of the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.argmin()
Array(0, dtype=int32)

Parameters:

args (Any)
kwargs (Any)

Return type:

astype(*args: Any, **kwargs: Any)

Copy the array and cast to a specified dtype.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.dtype
dtype('int32')

>>> q.astype(float)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property at: _QuantityIndexUpdateHelper

Helper property for index update functionality.

The at property provides a functionally pure equivalent of in-place array modifications.

In particular:

Alternate syntax	Equivalent In-place expression
`x = x.at[idx].set(y)`	`x[idx] = y`
`x = x.at[idx].add(y)`	`x[idx] += y`
`x = x.at[idx].subtract(y)`	`x[idx] -= y`
`x = x.at[idx].multiply(y)`	`x[idx] *= y`
`x = x.at[idx].divide(y)`	`x[idx] /= y`
`x = x.at[idx].power(y)`	`x[idx] **= y`
`x = x.at[idx].min(y)`	`x[idx] = minimum(x[idx], y)`
`x = x.at[idx].max(y)`	`x[idx] = maximum(x[idx], y)`
`x = x.at[idx].apply(ufunc)`	`ufunc.at(x, idx)`
`x = x.at[idx].get()`	`x = x[idx]`

None of the x.at expressions modify the original x; instead they return a modified copy of x. However, inside a jit() compiled function, expressions like x = x.at[idx].set(y) are guaranteed to be applied in-place.

Unlike NumPy in-place operations such as x[idx] += y, if multiple indices refer to the same location, all updates will be applied (NumPy would only apply the last update, rather than applying all updates.) The order in which conflicting updates are applied is implementation-defined and may be nondeterministic (e.g., due to concurrency on some hardware platforms).

By default, JAX assumes that all indices are in-bounds. Alternative out-of-bound index semantics can be specified via the mode parameter (see below).

Parameters:

mode –
string specifying out-of-bound indexing mode. Options are:
- "promise_in_bounds": (default) The user promises that indices are in bounds. No additional checking will be performed. In practice, this means that out-of-bounds indices in get() will be clipped, and out-of-bounds indices in set(), add(), etc. will be dropped.
- "clip": clamp out of bounds indices into valid range.
- "drop": ignore out-of-bound indices.
- "fill": alias for "drop". For get(), the optional fill_value argument specifies the value that will be returned.
See jax.lax.GatherScatterMode for more details.
wrap_negative_indices – If True (default) then negative indices indicate position from the end of the array, similar to Python and NumPy indexing. If False, then negative indices are considered out-of-bounds and behave according to the mode parameter.
fill_value – Only applies to the get() method: the fill value to return for out-of-bounds slices when mode is 'fill'. Ignored otherwise. Defaults to NaN for inexact types, the largest negative value for signed types, the largest positive value for unsigned types, and True for booleans.
indices_are_sorted – If True, the implementation will assume that the (normalized) indices passed to at[] are sorted in ascending order, which can lead to more efficient execution on some backends. If True but the indices are not actually sorted, the output is undefined.
unique_indices – If True, the implementation will assume that the (normalized) indices passed to at[] are unique, which can result in more efficient execution on some backends. If True but the indices are not actually unique, the output is undefined.

Examples

>>> x = jnp.arange(5.0)
>>> x
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[2].get()
Array(2., dtype=float32)
>>> x.at[2].add(10)
Array([ 0.,  1., 12.,  3.,  4.], dtype=float32)

By default, out-of-bound indices are ignored in updates, but this behavior can be controlled with the mode parameter:

>>> x.at[10].add(10)  # dropped
Array([0., 1., 2., 3., 4.], dtype=float32)
>>> x.at[20].add(10, mode='clip')  # clipped
Array([ 0.,  1.,  2.,  3., 14.], dtype=float32)

For get(), out-of-bound indices are clipped by default:

>>> x.at[20].get()  # out-of-bounds indices clipped
Array(4., dtype=float32)
>>> x.at[20].get(mode='fill')  # out-of-bounds indices filled with NaN
Array(nan, dtype=float32)
>>> x.at[20].get(mode='fill', fill_value=-1)  # custom fill value
Array(-1., dtype=float32)

Negative indices count from the end of the array, but this behavior can be disabled by setting wrap_negative_indices = False:

>>> x.at[-1].set(99)
Array([ 0.,  1.,  2.,  3., 99.], dtype=float32)
>>> x.at[-1].set(99, wrap_negative_indices=False, mode='drop')  # dropped!
Array([0., 1., 2., 3., 4.], dtype=float32)

block_until_ready()

Block until the array is ready.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.block_until_ready() is q
True

decompose(bases: Sequence[UnitBase | FunctionUnitBase | str], /)

Decompose the quantity into the given bases.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.decompose(["cm", "s"])
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: bases (Sequence[UnitBase | FunctionUnitBase | str])
Return type:: AbstractQuantity

property device: Device

Device where the array is located.

Examples

>>> import unxt as u
>>> u.Q(1, "m").device
CpuDevice(id=0)

devices()

Return the devices where the array is located.

Return type:: set[Device]

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.devices()
{CpuDevice(id=0)}

property dtype: dtype

Data type of the array.

Examples

>>> import unxt as u
>>> u.Q(1, "m").dtype
dtype('int32')

flatten()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.flatten()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

classmethod from_(cls: type[AbstractQuantity], *args: Any, **kwargs: Any)

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a unxt.AbstractQuantity from an array-like value and a unit.

Parameters:

value – The array-like value.
unit – The unit of the value.
dtype – The data type of the array (keyword-only).
args (Any)
kwargs (Any)

Return type:

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> u.Q.from_(x, "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_([1.0, 2, 3], "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_((1.0, 2, 3), "m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

Make a unxt.AbstractQuantity from an array-like value and a unit kwarg.

Examples

For this example we’ll use the unxt.Quantity class. The same applies to any subclass of unxt.AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_([1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], *, value: Any, unit: Any, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a AbstractQuantity from value and unit kwargs.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import unxt as u
>>> u.Q.from_(value=[1.0, 2, 3], unit="m")
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], mapping: Mapping[str, Any]) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from a Mapping.

Examples

For this example we’ll use the Quantity class. The same applies to any subclass of AbstractQuantity.

>>> import jax.numpy as jnp
>>> import unxt as u

>>> x = jnp.array([1.0, 2, 3])
>>> q = u.Q.from_({"value": x, "unit": "m"})
>>> q
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

>>> u.Q.from_({"value": q, "unit": "km"})
Quantity(Array([0.001, 0.002, 0.003], dtype=float32), unit='km')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: Any, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, "cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, unit: NoneType, /, *, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> u.Q.from_(q, None)
Quantity(Array(1, dtype=int32...), unit='m')

from_(cls: type[AbstractQuantity], value: AbstractQuantity, /, *, unit: Any | None = None, dtype: Any = None) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from another quantity.

The unit is unchanged.

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a StaticQuantity, keeping the value on NumPy dtypes.

The generic AbstractQuantity.from_ routes the value through jnp.asarray, which applies JAX’s x64-disabled dtype rules and silently downcasts int64 / float64 to int32 / float32. A StaticQuantity stores its value verbatim, so hand the value straight to __init__ and let the StaticValue.from_ converter convert it – that preserves the NumPy dtype. Delegating (rather than calling np.asarray here) also keeps .from_ and __init__ under the same policy for JAX inputs, instead of materialising an array the constructor would reject. (The keyword-unit overload delegates here, so it is covered too.)

Examples

>>> import numpy as np
>>> import unxt as u

>>> u.StaticQuantity.from_(
...     np.array([1, 2, 3], dtype=np.int64), "m"
... ).value.array.dtype
dtype('int64')

.from_ applies the same JAX-input policy as __init__ – it delegates rather than converting first, so the two cannot drift:

>>> import jax.numpy as jnp
>>> u.StaticQuantity.from_(jnp.array([1.0, 2.0]), "m")
StaticQuantity(array([1., 2.], dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"))
Quantity(Array(1., dtype=float32), unit='m')

from_(cls: type[AbstractQuantity], value: Quantity, u: Any, /, **kwargs: Any) → AbstractQuantity

Parameters:

args (Any)
kwargs (Any)

Return type:

Construct a quantity from an astropy Quantity, converting to a target unit.

The value is converted to the new unit.

Examples

>>> import unxt as u
>>> import astropy.units as apyu

>>> u.Q.from_(apyu.Quantity(1, "m"), "cm")
Quantity(Array(100., dtype=float32), unit='cm')

Parameters:

cls (type[AbstractQuantity])
args (Any)
kwargs (Any)

Return type:

is_equivalent(other: AbstractQuantity, /)

Whether self and other are physically equal (unit-aware).

The method form of unxt.equivalent; unlike == (which is unit-blind for StaticValue-backed quantities) this accounts for unit conversion.

Examples

>>> import unxt as u
>>> u.Q(1000.0, "m").is_equivalent(u.Q(1.0, "km"))
Quantity(Array(True, dtype=bool...), unit='')

Parameters:: other (AbstractQuantity)
Return type:: Any

property mT: AbstractQuantity

Matrix transpose of the array.

Examples

>>> import unxt as u
>>> q = u.Q([[0, 1], [1, 2]], "m")
>>> q.mT
Quantity(Array([[0, 1],
                          [1, 2]], dtype=int32), unit='m')

max(*args: Any, **kwargs: Any)

Return the maximum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.max()
Quantity(Array(3, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

mean(*args: Any, **kwargs: Any)

Return the mean value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.mean()
Quantity(Array(2., dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

min(*args: Any, **kwargs: Any)

Return the minimum value.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.min()
Quantity(Array(1, dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property ndim: int

Number of dimensions.

Examples

>>> import unxt as u
>>> q = u.Q([[1]], "m")
>>> q.ndim
2

ravel()

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Q([[1, 2], [3, 4]], "m")
>>> q.ravel()
Quantity(Array([1, 2, 3, 4], dtype=int32), unit='m')

reshape(*args: Any, order: str = 'C')

Return a reshaped version of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3, 4], "m")
>>> q.reshape(2, 2)
Quantity(Array([[1, 2],
                          [3, 4]], dtype=int32), unit='m')

Parameters:

args (Any)
order (str)

Return type:

round(*args: Any, **kwargs: Any)

Round the array to the given number of decimals.

Examples

>>> import unxt as u
>>> q = u.Q([1.1, 2.2, 3.3], "m")
>>> q.round(0)
Quantity(Array([1., 2., 3.], dtype=float32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

property shape: tuple[int, ...]: Shape of the array.

property sharding: Any

Return the sharding configuration of the array.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.sharding
SingleDeviceSharding(device=..., memory_kind=...)

property size: int

Total number of elements.

Examples

>>> import unxt as u
>>> q = u.Q([1, 2, 3], "m")
>>> q.size
3

squeeze(*args: Any, **kwargs: Any)

Return the array with all single-dimensional entries removed.

Examples

>>> import unxt as u
>>> q = u.Q([[[1], [2], [3]]], "m")
>>> q.squeeze()
Quantity(Array([1, 2, 3], dtype=int32), unit='m')

Parameters:

args (Any)
kwargs (Any)

Return type:

to(u: Any, /)

Convert the quantity to the given units.

See unxt.quantity.AbstractQuantity.uconvert.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

to_device(device: None | Device = None)

Move the array to a new device.

Examples

>>> import unxt as u
>>> q = u.Q(1, "m")
>>> q.to_device(None)
Quantity(Array(1, dtype=int32...), unit='m')

Parameters:: device (None | Device)
Return type:: AbstractQuantity

to_value(u: Any, /)

Return the value in the given units.

See unxt.AbstractQuantity.ustrip.

Examples

>>> from unxt import Quantity

>>> q = Quantity(1, "m")
>>> q.to_value("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Union[Array, ndarray, bool, number, bool, int, float, complex]

uconvert(u: Any, /)

Convert the quantity to the given units.

See also

None: convert a quantity to a new unit.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.uconvert("cm")
Quantity(Array(100., dtype=float32, ...), unit='cm')

Parameters:: u (Any)
Return type:: AbstractQuantity

ustrip(u: Any, /)

Return the value in the given units.

See also

None: strip the units from a quantity.

Examples

>>> import unxt as u

>>> q = u.Q(1, "m")
>>> q.ustrip("cm")
Array(100., dtype=float32, weak_type=True)

Parameters:: u (Any)
Return type:: Array

unxt.uconvert_value(uto: Any, ufrom: Any, x: Any, /)

Convert the value from specified units to specified units.

General signature: (to_unit, from_unit, value) -> converted_value. Other signatures are defined via method dispatch. See uconvert_value.methods for details.

Examples

>>> import unxt as u  # implements `unxts.api.uconvert_value`

>>> u.uconvert_value(u.unit("m"), u.unit("km"), 1)
1000.0

>>> u.uconvert_value("m", "km", 1)
1000.0

For further examples, see the other method dispatches.

unxt.uconvert_value(tousys: AbstractUnitSystem, ufrom: UnitBase | FunctionUnitBase, x: ArrayLike, /) → ArrayLike

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Convert the value from units to a unitsystem’s preferred units.

Examples

>>> import unxt as u
>>> u.uconvert_value(u.unitsystems.galactic, u.unit("km"), 1e17)  # kpc
3.2407792894443648

>>> u.unitsystems.galactic[u.dimension("length")]  # checking the units
Unit("kpc")

unxt.uconvert_value(tousys: AbstractUnitSystem, ufrom: str, x: ArrayLike, /) → ArrayLike

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Convert the value from units to a unitsystem’s preferred units.

Examples

>>> import unxt as u
>>> u.uconvert_value(u.unitsystems.galactic, "km", 1e17)  # in kpc
3.2407792894443648

>>> u.unitsystems.galactic[u.dimension("length")]  # checking the units
Unit("kpc")

unxt.uconvert_value(uto: str, ufrom: str, x: ArrayLike, /) → ArrayLike

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Convert the value to the specified units.

Examples

>>> import unxt as u

>>> u.uconvert_value("m", "km", 1)
1000.0

unxt.uconvert_value(uto: Any, ufrom: Any, x: AbstractQuantity, /) → AbstractQuantity

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Convert the quantity to the specified units.

This is a convenience dispatch so that users can use the lower-level function with a Quantity and not break their code. This dispatch simply calls uconvert, checking first that the units are convertible.

Examples

>>> import unxt as u

>>> q = u.Q(1, "km")
>>> u.uconvert_value("m", "km", q)
Quantity(Array(1000., dtype=float32...), unit='m')

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Convert the value to the specified units.

Examples

>>> import astropy.units as apyu
>>> import unxt as u

>>> u.uconvert_value(apyu.Unit("km"), apyu.Unit("m"), 1000)
1.0

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Convert an astropy quantity to the specified units.

This is a convenience dispatch mirroring the AbstractQuantity one: users may pass a quantity to the lower-level value function and it keeps working. Like that dispatch, it checks that the quantity’s own unit is convertible to the caller-supplied ufrom – ~unxt.is_unit_convertible takes the target first, so is_unit_convertible(ufrom, x.unit) reads “x.unit -> ufrom” – then defers to the quantity’s own unit for the arithmetic, returning a relabelled quantity rather than a bare value.

This dispatch is required: an astropy ~astropy.units.Quantity subclasses numpy.ndarray, so it satisfies the x: ArrayLike annotation of the bare-value dispatch above and is not rejected even under beartype. That body’s ufrom.to(uto, x) converts the magnitude but returns a quantity still carrying ufrom’s unit, mislabelling the result (e.g. uconvert_value("m", "km", 1 km) gave 1000.0 km).

Examples

>>> import astropy.units as apyu
>>> import unxt as u

>>> u.uconvert_value(apyu.Unit("m"), apyu.Unit("km"), apyu.Quantity(1.0, "km"))
<Quantity 1000. m>

>>> u.uconvert_value(apyu.Unit("m"), apyu.Unit("m"), apyu.Quantity(5.0, "m"))
<Quantity 5. m>

Parameters:

uto (Any)
ufrom (Any)
x (Any)

Return type:

Any

unxt.uconvert(u: Any, x: Any, /)

Convert the quantity to the specified units.

General signature: (to_unit, quantity) -> converted_quantity. Other signatures are defined via method dispatch. See uconvert.methods for details.

Internally, {func}`unxts.api.uconvert` often calls to {func}`unxts.api.uconvert_value` to perform the numerical conversion on the Quantity’s value.

Examples

>>> import unxt as u  # implements `unxts.api.uconvert`

>>> q = u.Q(1, "km")
>>> u.uconvert(u.unit("m"), q)
Quantity(Array(1000., dtype=float32, ...), unit='m')

>>> u.uconvert("m", q)
Quantity(Array(1000., dtype=float32, ...), unit='m')

For further examples, see the other method dispatches.

unxt.uconvert(ustr: str, x: AbstractQuantity, /) → AbstractQuantity

Parameters:

u (Any)
x (Any)

Return type:

Convert the quantity to the specified units.

Examples

>>> from unxt import Quantity, units

>>> x = Quantity(1000, "m")
>>> uconvert("km", x)
Quantity(Array(1., dtype=float32...), unit='km')

unxt.uconvert(usys: AbstractUnitSystem, x: AbstractQuantity, /) → AbstractQuantity

Parameters:

u (Any)
x (Any)

Return type:

Convert the quantity to the specified units.

Examples

>>> from unxt import Quantity, units
>>> from unxt.unitsystems import galactic

>>> q = Quantity(1e17, "km")
>>> uconvert(galactic, q)
Quantity(Array(3.2407792, dtype=float32...), unit='kpc')

unxt.uconvert(u: UnitBase | Unit | FunctionUnitBase | StructuredUnit, x: AbstractQuantity, /) → AbstractQuantity

Parameters:

u (Any)
x (Any)

Return type: