coordinax.distances

`coordinax.distances`#

coordinax.distances module.

class coordinax.distances.AbstractDistance#

Bases: AbstractQuantity

Distance quantities.

property distance: AbstractDistance#

The distance.

Examples

>>> import coordinax.distances as cxd
>>> d = cxd.Distance(10, "km")
>>> d.distance is d
True

>>> import coordinax.astro as cxastro
>>> cxastro.DistanceModulus(10, "mag").distance
Distance(1000., 'pc')

>>> p = cxastro.Parallax(1, "mas")
>>> p.distance.to("kpc")
Distance(1., 'kpc')

property T: AbstractQuantity#

Transpose of the array.

Examples

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

Parameters:

args (Any)
kwargs (Any)

Return type:

Array

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

Return the indices of the minimum value.

Examples

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

Parameters:

args (Any)
kwargs (Any)

Return type:

Array

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

Copy the array and cast to a specified dtype.

Examples

>>> import unxt as u
>>> q = u.Quantity([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:

AbstractQuantity

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.Quantity(1, "m")
>>> q.block_until_ready() is q
True

decompose(bases: Sequence[Unit | UnitBase | CompositeUnit | 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[Unit | UnitBase | CompositeUnit | str])
Return type:: AbstractQuantity

property device: Device#

Device where the array is located.

Examples

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

property dtype: dtype#

Data type of the array.

Examples

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

flatten()#

Return a flattened version of the array.

Return type:: AbstractQuantity

Examples

>>> import unxt as u
>>> q = u.Quantity([[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:

coordinax.distances

Contents

coordinax.distances#

`coordinax.distances`#