xarray_fftN

PlasmaCalcs.tools.fft_tools.xarray_fft.xarray_fftN(array, dim=None, ds=None, *, rad=None, abs=False, slices={}, keep=UNSET, half=UNSET, step=UNSET, missing_slices=UNSET, **kw_np_fftn)

calculates fft(array) along N dimensions.

shifts frequencies such that the 0-frequency is in the center.
replaces result dimensions & coordinates appropriately, to indicate which dims were fft’d.
dim: None, str, or iterable of strs
coordinates(s) to take fft over.
Can be pre-fft or post-fft names. (e.g. ‘x’, ‘freq_x’, ‘freqrad_x’, ‘k_x’)
promote_dim(array, coord) for any non-dimension coordinates, as needed.
None –> equivalent to array.dims
str –> just this coordinate.
iterable of strs –> just these coordinates.
ds: None, number, or dict of {dim: d}
spacing between elements of array (pre-transform), along each dim.
if number, use the same value for all dims.
if None, infer via array.coords[dim].diff(dim) for each dim
(requires evenly-spaced coordinates in dim; spacing checked with np.allclose)
rad: None or bool
whether to convert frequencies to “radians” by multiplying them by 2 * pi.
E.g., for fft in space, rad=False gives 1/wavelength; rad=True gives wavenumber k.
if None, infer from dim if any post-fft names provided, else default to False.
abs: bool
if True, return np.abs(result), instead.
slices: dict or FFTSlices
instructions for slicing the final result.
Can provide {cname: indexer} instructing to slice post-fft dimension
associated with cname, via indexer. (cname can be pre-fft or post-fft name.)
These understand fractional indexing: can provide a fractional value
between -1 and 1, to use that fraction of the length along the relevant dimension.
Can also provide keep, half, step, and/or missing_slices, here (Or, as kwargs).
(raise InputConflictError if any provided in both places and have conflicting values.)
See those kwargs for more details.
keep: None, True, dict, or number in 0 < keep <= 1
implies the fraction of each dimension to keep.
(ignored for any dimensions which already have a slice specified.)
e.g. keep=0.4 with length=1000 would result in slice(300, 700),
since that keeps 400 out of 1000 points, and is centered at 1000/2.
None –> ignored.
True –> use keep = DEFAULTS.FFT_KEEP (default: 0.4).
dict –> different value in each dimension;
keys can be pre-fft OR post-fft dimension names.
UNSET –> use None.
half: None, str, or iterable of strs
dimensions along which to keep only the right half of the result.
(ignored for any dimensions which already have a slice specified.)
None –> ignored.
str or iterable of strs –>can be pre-fft OR post-fft dimension name(s).
Applied after keep, e.g. keep=0.4, length=1000, half=’x’ –> slice(500, 700) for x.
UNSET –> use None.
step: None, dict, int, or non-integer between -1 and 1
step to take along each dim.
(ignored for any dimensions which already have a slice specified.)
fractional value –> use fraction of length (e.g. 0.01 –> 1% of dim length), min |step|=1.
negative –> reverses direction (and swaps start & stop for the slice)
None –> equivalent to using step=1.
dict –> different value in each dimension;
keys can be pre-fft OR post-fft dimension names.
UNSET –> use None.
missing_slices: ‘ignore’, ‘warn’, or ‘raise’
tells how to handle keys not matching any fft-related coordinate.
‘ignore’ –> silently ignore these keys. This is the default.
‘warn’ –> issue a warning.
‘raise’ –> raise an error.
UNSET –> use ‘ignore’
additional kwargs passed to np.fft.fftn.
returns result of fftn(…), shifted such that the 0-frequency is in the center,
and with the relevant dimensions renamed as specified.