Function curvefit

xarray/computation/fit.py:311–537

Curve fitting optimization for arbitrary functions. Wraps `scipy.optimize.curve_fit` with `apply_ufunc`. Parameters ---------- obj : Dataset or DataArray Object to perform the curvefit on coords : hashable, DataArray, or sequence of hashable or DataArray In

(
    obj,
    coords: str | DataArray | Iterable[str | DataArray],
    func: Callable[..., Any],
    reduce_dims: Dims = None,
    skipna: bool = True,
    p0: Mapping[str, float | DataArray] | None = None,
    bounds: Mapping[str, tuple[float | DataArray, float | DataArray]] | None = None,
    param_names: Sequence[str] | None = None,
    errors: ErrorOptions = "raise",
    kwargs: dict[str, Any] | None = None,
)

def curvefit(
    obj,
    coords: str | DataArray | Iterable[str | DataArray],
    func: Callable[..., Any],
    reduce_dims: Dims = None,
    skipna: bool = True,
    p0: Mapping[str, float | DataArray] | None = None,
    bounds: Mapping[str, tuple[float | DataArray, float | DataArray]] | None = None,
    param_names: Sequence[str] | None = None,
    errors: ErrorOptions = "raise",
    kwargs: dict[str, Any] | None = None,
):
    """
    Curve fitting optimization for arbitrary functions.

    Wraps `scipy.optimize.curve_fit` with `apply_ufunc`.

    Parameters
    ----------
    obj : Dataset or DataArray
        Object to perform the curvefit on
    coords : hashable, DataArray, or sequence of hashable or DataArray
        Independent coordinate(s) over which to perform the curve fitting. Must share
        at least one dimension with the calling object. When fitting multi-dimensional
        functions, supply `coords` as a sequence in the same order as arguments in
        `func`. To fit along existing dimensions of the calling object, `coords` can
        also be specified as a str or sequence of strs.
    func : callable
        User specified function in the form `f(x, *params)` which returns a numpy
        array of length `len(x)`. `params` are the fittable parameters which are optimized
        by scipy curve_fit. `x` can also be specified as a sequence containing multiple
        coordinates, e.g. `f((x0, x1), *params)`.
    reduce_dims : str, Iterable of Hashable or None, optional
        Additional dimension(s) over which to aggregate while fitting. For example,
        calling `ds.curvefit(coords='time', reduce_dims=['lat', 'lon'], ...)` will
        aggregate all lat and lon points and fit the specified function along the
        time dimension.
    skipna : bool, default: True
        Whether to skip missing values when fitting. Default is True.
    p0 : dict-like, optional
        Optional dictionary of parameter names to initial guesses passed to the
        `curve_fit` `p0` arg. If the values are DataArrays, they will be appropriately
        broadcast to the coordinates of the array. If none or only some parameters are
        passed, the rest will be assigned initial values following the default scipy
        behavior.
    bounds : dict-like, optional
        Optional dictionary of parameter names to tuples of bounding values passed to the
        `curve_fit` `bounds` arg. If any of the bounds are DataArrays, they will be
        appropriately broadcast to the coordinates of the array. If none or only some
        parameters are passed, the rest will be unbounded following the default scipy
        behavior.
    param_names : sequence of hashable, optional
        Sequence of names for the fittable parameters of `func`. If not supplied,
        this will be automatically determined by arguments of `func`. `param_names`
        should be manually supplied when fitting a function that takes a variable
        number of parameters.
    errors : {"raise", "ignore"}, default: "raise"
        If 'raise', any errors from the `scipy.optimize_curve_fit` optimization will