xarray.Dataset.polyfit#
- Dataset.polyfit(dim, deg, skipna=None, rcond=None, w=None, full=False, cov=False)[source]#
Least squares polynomial fit.
This replicates the behaviour of numpy.polyfit but differs by skipping invalid values when skipna = True.
- Parameters
dim (hashable) – Coordinate along which to fit the polynomials.
deg (
int
) – Degree of the fitting polynomial.skipna (
bool
orNone
, 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
orNone
, 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
polyfit_results (
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)
- Warns
RankWarning – The rank of the coefficient matrix in the least-squares fit is deficient. The warning is not raised with in-memory (not dask) data and full=True.
See also