Method curvefit

xarray/core/dataarray.py:6524–6694

Curve fitting optimization for arbitrary functions. Wraps :py:func:`scipy.optimize.curve_fit` with :py:func:`~xarray.apply_ufunc`. Parameters ---------- coords : Hashable, DataArray, or sequence of DataArray or Hashable Independent coordinate(s)

(
        self,
        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,
    )

        return ds[self.name]

    def curvefit(
        self,
        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,
    ) -> Dataset:
        """
        Curve fitting optimization for arbitrary functions.

        Wraps :py:func:`scipy.optimize.curve_fit` with :py:func:`~xarray.apply_ufunc`.

        Parameters
        ----------
        coords : Hashable, DataArray, or sequence of DataArray or Hashable
            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 or None, 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 or None, 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
            raise an exception. If 'ignore', the coefficients and covariances for the
            coordinates where the fitting failed will be NaN.