Array class

Array class#

class Array(values, dims: Tuple[Dimension, ...], spaces: Tuple[Literal['pos', 'freq'], ...], eager: Tuple[bool, ...], factors_applied: Tuple[bool, ...], xp)[source]#

The Array class facilitates the generalized Discrete Fourier transform and all operations on its values while retaining coordinate grid information.

Parameters:

values (Any) – The values array with respect to the dimensions.
dims (Tuple[Dimension, ...]) – Dimensions defining the coordinates of the values.
spaces (Tuple[Space, ...]) – Space of the given values per Dimension.
eager (Tuple[bool, ...]) – Whether the factors should be applied to the internal values after performing a space transition. This mainly influences the behavior of into_space. It also acts as a tie breaker on addition and subtraction when factors_applied=False and factors_applied=True as a result would cost equally many operations.
factors_applied (Tuple[bool, ...]) – Whether the lazy factors have been applied to the given values.
xp (Any) – The array API namespace.

Examples

First, we initialize the dimensions using fa.dim_from_constraints:

>>> import fftarray as fa
>>> x_dim: fa.Dimension = fa.dim_from_constraints("x", pos_min=-1, pos_max=1., n=64, freq_middle=0.)
>>> y_dim: fa.Dimension = fa.dim_from_constraints("y", pos_min=-10, pos_max=10., n=128, freq_middle=0.)

Based on these, the coordinates can be retrieved in form of 1D Arrays. These can be combined to a 2D Array via automatic broadcasting:

>>> x: fa.Array = fa.coords_from_dim(x_dim, "pos")
>>> y: fa.Array = fa.coords_from_dim(y_dim, "pos")
>>> xy = x**2 * fa.sin(y)

The space (as well as other attributes such as xp, eager and factors_applied) can be set via respective into functions:

>>> xy_freq: fa.Array = xy.into_space("freq")

Integrate the Array from x=-0.2 to x=0.2 by indexing the given region before intgrating it via fa.integrate:

>>> res = fa.integrate(xy.sel(x=slice(-0.2,0.2)))

Methods

`Array.into_dtype`(dtype, /)	Change the values' dtype.
`Array.into_eager`(eager, /)	Change the eager state.
`Array.into_factors_applied`(factors_applied, /)	Change the factors_applied state.
`Array.into_space`(space, /)	Transform the space(s) of the Array's values.
`Array.into_xp`(xp, /)	Change the array API namespace.
`Array.isel`([indexers, missing_dims])	Select specific values along respective dimension names by indices.
`Array.sel`([indexers, missing_dims, method])	Select specific values along respective dimension names by label/coordinate.
`Array.values`(space, /, *[, xp, dtype])	The raw values with all lazy fft factors applied.

Attributes

`Array.device`	The device of the underlying array.
`Array.dims`	Tuple of the Array's Dimensions.
`Array.dims_dict`	Dictionary mapping each dimension name to the Array's Dimension.
`Array.dtype`	The dtype of the Array's values.
`Array.eager`	Whether the space transformation should be lazy or not.
`Array.factors_applied`	Whether the lazy fft factors have been applied to the internal values.
`Array.loc`	Attribute for location based indexing like xarray.
`Array.shape`	Shape of the Array's values.
`Array.sizes`	Dictionary mapping each dimension name to the number of grid points.
`Array.spaces`	The spaces in which the values currently are in, per dimension.
`Array.xp`	The array API namespace driving the mathematical operations.

Array class

Contents

Array class#