numpy.ndarray

err_before_fit(data,
minsv=None,
minsvr=None,
constant=True)
How much variation did the data have before the fit? This is used to
compare with the error after the fit, to allow a Ftest or ANOVA. 
source code



pack(data,
constant=True)
Prepare the data array and (optionally) weight the data. 
source code


(A, B, errs, sv, rank) where

A is a 2D
numpy.ndarray matrix.

B is a 1D
numpy.ndarray vector (if constant, else
None ).

errs is a
numpy.ndarray vector, one value for each
output coordinate. The length is the same as the out
vector (see pack).

sv is a
numpy.ndarray vector. The length is the same
as the in vector (see pack).

localfit(data,
minsv=None,
minsvr=None,
constant=True)
Does a linear fit to data via a singular value decomposition
algorithm. 
source code



reg_localfit(data,
regstr=0.0,
regtgt=None,
rscale=None,
constant=True)
Does a linear fit to data via a singular value decomposition
algorithm. 
source code





fit_giving_sigmas(data,
minsv=None,
minsvr=None,
constant=True)
Does a linear fit to data via a singular value decomposition
algorithm. 
source code



















