PlasmaCalcs.tools.xarray_tools.xarray_sci.xarray_curve_fit

PlasmaCalcs.tools.xarray_tools.xarray_sci.xarray_curve_fit(array, f, dim, *, stddev=True, promote_dims_if_needed=True, pnames=None, pbounds=None, bounds=UNSET, **kw_curve_fit)

scipy.optimize.curve_fit(f, xdata=array[dim], ydata=array).

Except, iterate over all other dims in array.

E.g. arr.curve_fit(‘t’, f) for arr with ‘t’ and ‘fluid’ dims

–> result reduces ‘t’ dim but retains ‘fluid’ dim.

f: callable like f(x, param1, param2, …)

function to fit.

stddev: bool

whether to include data_var ‘stddev’ telling standard deviation of the fit.

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.

pnames: None or list of str

names of params. If provided, ‘param’ coord will be assigned these names.

pbounds: list of [None, callable, or 2-tuple of value, None, or callable]

bounds for each parameter. Provide pbounds or bounds, but not both.

Each bound can be:

callable –> call as bound(array, dim) (after doing array.pc.ensure_dims(dim)).

None –> use (-np.inf, np.inf).

2-tuple –> (lower, upper).

callable –> use lower(array, dim) / upper(array, dim)

None –> use -np.inf / np.inf.

bounds: UNSET or (list of lower bounds, list of upper bounds)

bounds, formatted as expected by scipy curve_fit.

Provide pbounds or bounds, but not both.

additional kwargs go to scipy.optimize.curve_fit.

returns xarray.Dataset with data_vars:

params: iterable along ‘param’ dimension of the parameters of the fit.

e.g. call f with f(x, *result[‘params’].values) if 1D array.

e.g. f(x, *result[‘params’].isel(fluid=0).values) if 2D array.

stddev: standard deviation of each parameter’s fit.