- 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.
dim (hashable) – Coordinate along which to fit the polynomials.
int) – Degree of the fitting polynomial.
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.
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.
False) – Whether to return the residuals, matrix rank and singular values in addition to the coefficients.
False) – Whether to return to the covariance matrix in addition to the coefficients. The matrix is not scaled if cov=’unscaled’.
Dataset) – A single dataset which contains (for each “var” in the input dataset):
The coefficients of the best fit for each variable in this dataset.
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.
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.
The singular values of the scaled Vandermonde coefficient matrix (only included if full=True)
The covariance matrix of the polynomial coefficient estimates (only included if full=False and cov=True)
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.