numpy.polynomial.chebyshev.chebfit¶

numpy.polynomial.chebyshev.chebfit(x, y, deg, rcond=None, full=False, w=None)[source]¶

Least squares fit of Chebyshev series to data.

Return the coefficients of a Chebyshev series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are in the form

$p(x) = c_0 + c_1 * T_1(x) + ... + c_n * T_n(x),$

where n is deg.

Parameters:

Parameters:	x : array_like, shape (M,) x-coordinates of the M sample points `(x[i], y[i])`. y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column. deg : int or 1-D array_like Degree(s) of the fitting polynomials. If deg is a single integer all terms up to and including the deg‘th term are included in the fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. rcond : float, optional Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)eps, where eps is the relative precision of the float type, about 2e-16 in most cases. full* : bool, optional Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (M,), optional Weights. If not None, the contribution of each point `(x[i],y[i])` to the fit is weighted by w[i]. Ideally the weights are chosen so that the errors of the products `w[i]*y[i]` all have the same variance. The default value is None. New in version 1.5.0.
Returns:	coef : ndarray, shape (M,) or (M, K) Chebyshev coefficients ordered from low to high. If y was 2-D, the coefficients for the data in column k of y are in column k. [residuals, rank, singular_values, rcond] : list These values are only returned if full = True resid – sum of squared residuals of the least squares fit rank – the numerical rank of the scaled Vandermonde matrix sv – singular values of the scaled Vandermonde matrix rcond – value of rcond. For more details, see linalg.lstsq.
Warns:	RankWarning The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if full = False. The warnings can be turned off by >>> import warnings >>> warnings.simplefilter('ignore', RankWarning)

x : array_like, shape (M,)

x-coordinates of the M sample points (x[i], y[i]).

y : array_like, shape (M,) or (M, K)

y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column.

deg : int or 1-D array_like

Degree(s) of the fitting polynomials. If deg is a single integer all terms up to and including the deg‘th term are included in the fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead.

rcond : float, optional

Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases.

full : bool, optional

Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned.

w : array_like, shape (M,), optional

Weights. If not None, the contribution of each point (x[i],y[i]) to the fit is weighted by w[i]. Ideally the weights are chosen so that the errors of the products w[i]*y[i] all have the same variance. The default value is None.

New in version 1.5.0.

Returns:

coef : ndarray, shape (M,) or (M, K)

Chebyshev coefficients ordered from low to high. If y was 2-D, the coefficients for the data in column k of y are in column k.

[residuals, rank, singular_values, rcond] : list

These values are only returned if full = True

resid – sum of squared residuals of the least squares fit rank – the numerical rank of the scaled Vandermonde matrix sv – singular values of the scaled Vandermonde matrix rcond – value of rcond.

For more details, see linalg.lstsq.

Warns:

RankWarning

The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if full = False. The warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', RankWarning)