polyfit

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 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, array-like or None, 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 – A single dataset which contains:

polyfit_coefficients: The coefficients of the best fit.
polyfit_residuals: The residuals of the least-square computation (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)
[dim]_singular_value: The singular values of the scaled Vandermonde coefficient matrix (only included if full=True)
polyfit_covariance: The covariance matrix of the polynomial coefficient estimates (only included if full=False and cov=True)

Return type:

Dataset