Function polyfit

xarray/computation/fit.py:101–308

Least squares polynomial fit. This replicates the behaviour of `numpy.polyfit` but differs by skipping invalid values when `skipna = True`. Parameters ---------- obj : Dataset or DataArray Object to perform the polyfit on dim : hashable Coordinate along

(
    obj,
    dim: Hashable,
    deg: int,
    skipna: bool | None = None,
    rcond: np.floating[Any] | float | None = None,
    w: Hashable | Any = None,
    full: bool = False,
    cov: bool | Literal["unscaled"] = False,
)

def polyfit(
    obj,
    dim: Hashable,
    deg: int,
    skipna: bool | None = None,
    rcond: np.floating[Any] | float | None = None,
    w: Hashable | Any = None,
    full: bool = False,
    cov: bool | Literal["unscaled"] = False,
):
    """
    Least squares polynomial fit.

    This replicates the behaviour of `numpy.polyfit` but differs by skipping
    invalid values when `skipna = True`.

    Parameters
    ----------
    obj : Dataset or DataArray
        Object to perform the polyfit on
    dim : hashable
        Coordinate along which to fit the polynomials.
    deg : int
        Degree of the fitting polynomial.
    skipna : bool or None, optional
        If True, removes all invalid values before fitting each 1D slices of the array.
        Default is True if data is stored in a dask.array or if there is any
        invalid values, False otherwise.
    rcond : float or None, optional
        Relative condition number to the fit.
    w : hashable or Any, optional
        Weights to apply to the y-coordinate of the sample points.
        Can be an array-like object or the name of a coordinate in the dataset.
    full : bool, default: False
        Whether to return the residuals, matrix rank and singular values in addition
        to the coefficients.
    cov : bool or "unscaled", default: False
        Whether to return to the covariance matrix in addition to the coefficients.
        The matrix is not scaled if `cov='unscaled'`.

    Returns
    -------
    Dataset
        A single dataset which contains (for each "var" in the input dataset):

        [var]_polyfit_coefficients
            The coefficients of the best fit for each variable in this dataset.
        [var]_polyfit_residuals
            The residuals of the least-square computation for each variable (only included if `full=True`)
            When the matrix rank is deficient, np.nan is returned.
        [dim]_matrix_rank
            The effective rank of the scaled Vandermonde coefficient matrix (only included if `full=True`)
            The rank is computed ignoring the NaN values that might be skipped.
        [dim]_singular_values
            The singular values of the scaled Vandermonde coefficient matrix (only included if `full=True`)
        [var]_polyfit_covariance
            The covariance matrix of the polynomial coefficient estimates (only included if `full=False` and `cov=True`)