qspec.analyze.york_fit  (  x ,  y ,  sigma_x = None ,  sigma_y = None ,  corr = None ,  iter_max = 200 ,  report = False ,  show = False ,  ** kwargs  )[source]

A linear regression algorithm to find the best straight line, given normally distributed errors for x and y and correlation coefficients between errors in x and y. The algorithm is described in ['Unified equations for the slope, intercept, and standard errors of the best straight line', York et al., American Journal of Physics 72, 367 (2004)]. See the comments to compare the individual steps.

Parameters:

xndarray | Iterable

The x data.

yndarray | Iterable

The y data.

sigma_xndarray | Iterable

The 1-sigma uncertainties of the x data.

sigma_yndarray | Iterable

The 1-sigma uncertainties of the y data.

corrndarray | Iterable

The correlation coefficients between the x and y data.

iter_maxint

The maximum number of iterations to find the best slope.

reportbool

Whether to print the result of the fit.

showbool

Whether to plot the fit result.

kwargsNone

regression algorithm to find the best straight line, given normally distributed errors for x and y and correlation coefficients between errors in x and y. The algorithm is described in ['Unified equations for the slope, intercept, and standard errors of the best straight line', York et al., American Journal of Physics 72, 367 (2004)]. See the comments to compare the individual steps.

Returns:

outNone

popt, pcov. The best y-intercept and slope, their covariance matrix and the used alpha.