PlasmaCalcs.tools.xarray_tools.xarray_sci.xarray_interp_inverse

PlasmaCalcs.tools.xarray_tools.xarray_sci.xarray_interp_inverse(array, interpto=None, output=None, *, promote_dims_if_needed=True, assume_sorted=None, assume_sorted_values=None, method=None, kw_interp=None, **interpto_as_kw)

interpolate a DataArray but using the array values as one of the interpolation variables;

the result is the array of the unused interpolation coordinate.

Example: if array has dims {‘x’, ‘y’} and name ‘T’, and interpto specifies ‘x’ and ‘T’,

then result will be a DataArray with dims {‘x’, ‘T’} and values for ‘y’.

Special case: if interpto specifications are single values,

the result will be scalar along that key instead of a dimension,

e.g. if interpto[‘x’] = 7 (as a opposed to a 1D array like [1,2,3]),

then result would have coordinate ‘x’=7 but not have an ‘x’ dimension.

[EFF] note: inefficient if choosing many values along vars other than array.name.

each result value along those vars corresponds to its own interp call.

The internal steps are roughly:

(1) array.interp(all interpto vars except array.name)

(2) array.assign_coords({array.name: array})

(3) for each index along all interpto vars except array.name:

tmp = array.isel(index).interp({array.name: interpto[array.name]})

result[index] = tmp[output var]

[TODO] still need to implement step 3 for 3D+ arrays instead of only 2D or less.

array: xarray.DataArray

must have non-None array.name.

interpto: None or dict

dictionary of {var: value or 1D array of values} to interpolate to.

Keys must correspond to array.name, and coords for all except 1 dim of array.

None –> provide interpto dict as kwargs to this function.

output: None or str

name for the result variable.

None –> use the key from array.dims which is missing from interpto (after xarray_ensure_dims).

promote_dims_if_needed: bool

whether to promote non-dimension coords to dimensions.

if False, raise DimensionKeyError if any relevant coord is not already a dimension.

assume_sorted: None or bool

whether to assume_sorted during step (1),

i.e. during the initial interp for all interpto vars except array.name

None –> assume_sorted if xarray_is_sorted.

True –> assume_sorted without checking. CAUTION: only do this if you’re 100% sure!

False –> don’t assume sorted. May be noticeably slower for large arrays.

assume_sorted_values: None or bool

whether to assume_sorted during step (3),

i.e. during the interp using array.name as a coordinate.

None –> assume_sorted if xarray_is_sorted. check at each index in step 3;

if False multiple times in a row, stop checking and just assume False.

True –> assume_sorted without checking. CAUTION: only do this if you’re 100% sure!

False –> don’t assume sorted. May be noticeably slower for large arrays.

method: None or str

method to pass to xarray.interp for all interpolations.

if None, use xarray.interp method default.

kw_interp: None or dict

if provided, pass these kwargs to all calls of xarray.interp.

These will eventually go to the internal interpolator method e.g. from scipy.

interpto_as_kw: optionally, provide interpto dict as kwargs to this function.