nonlinear_polyfit

sofia_redux.toolkit.fitting.polynomial.nonlinear_polyfit(samples, order, exponents=None, error=1, product=None, mask=None, covar=False, ignorenans=True, info=None, **kwargs)

Solve for polynomial coefficients using non-linear least squares fit

Parameters:

samplesarray_like of float (ndim + 1, n_points)

samples[0] should contain the independent values of the samples in the first dimension. samples[-1] should contain the dependent values of the samples If solving for two features, samples[1] contains the independent values of the samples in the second dimension. i.e. x = samples[0], y = samples[1], z = samples[2].

orderint or array_like of int

Either a scalar polynomial order to fit across all features or an array specifying the order to fit across each dimension.

exponentsarray_like of (int or float) (n_exponents, ndimensions)

If set will override order.

errorfloat or array_like of float, optional

(n_points,) error in z

covarbool, optional

If True, return the covariance

ignorenansbool, optional

If True, remove any sample points containing NaNs.

infodict, optional

If provided will be updated with product and exponents. Note that product will always be None for nonlinear fitting.

productnumpy.ndarray of numpy.float64, optional

Not used.

maskarray_like of bool

array where True indicates a value to use in fitting and False is a value to ignore. Overrides ignorenans.

kwargsdict, optional

Additional keyword arguments to scipy.optimize.curve_fit

Returns:

coefficients, exponents, [covariance]tuple of numpy.ndarray

The polynomial coefficients (ncoeffs,), the polynomial exponents (ncoeffs, ndim), and (optionally) the covariance matrix (ncoeffs, ncoeffs)