scipy.odr.RealData¶

class scipy.odr.RealData(x, y=None, sx=None, sy=None, covx=None, covy=None, fix=None, meta={})[source]¶

The data, with weightings as actual standard deviations and/or covariances.

Parameters:

Parameters:	x : array_like Observed data for the independent variable of the regression y : array_like, optional If array-like, observed data for the dependent variable of the regression. A scalar input implies that the model to be used on the data is implicit. sx : array_like, optional Standard deviations of x. sx are standard deviations of x and are converted to weights by dividing 1.0 by their squares. sy : array_like, optional Standard deviations of y. sy are standard deviations of y and are converted to weights by dividing 1.0 by their squares. covx : array_like, optional Covariance of x covx is an array of covariance matrices of x and are converted to weights by performing a matrix inversion on each observation’s covariance matrix. covy : array_like, optional Covariance of y covy is an array of covariance matrices and are converted to weights by performing a matrix inversion on each observation’s covariance matrix. fix : array_like, optional The argument and member fix is the same as Data.fix and ODR.ifixx: It is an array of integers with the same shape as x that determines which input observations are treated as fixed. One can use a sequence of length m (the dimensionality of the input observations) to fix some dimensions for all observations. A value of 0 fixes the observation, a value > 0 makes it free. meta : dict, optional Free-form dictionary for metadata.

x : array_like

Observed data for the independent variable of the regression

y : array_like, optional

If array-like, observed data for the dependent variable of the regression. A scalar input implies that the model to be used on the data is implicit.

sx : array_like, optional

Standard deviations of x. sx are standard deviations of x and are converted to weights by dividing 1.0 by their squares.

sy : array_like, optional

Standard deviations of y. sy are standard deviations of y and are converted to weights by dividing 1.0 by their squares.

covx : array_like, optional

Covariance of x covx is an array of covariance matrices of x and are converted to weights by performing a matrix inversion on each observation’s covariance matrix.

covy : array_like, optional

Covariance of y covy is an array of covariance matrices and are converted to weights by performing a matrix inversion on each observation’s covariance matrix.

fix : array_like, optional

The argument and member fix is the same as Data.fix and ODR.ifixx: It is an array of integers with the same shape as x that determines which input observations are treated as fixed. One can use a sequence of length m (the dimensionality of the input observations) to fix some dimensions for all observations. A value of 0 fixes the observation, a value > 0 makes it free.

meta : dict, optional

Free-form dictionary for metadata.

Notes

The weights wd and we are computed from provided values as follows:

sx and sy are converted to weights by dividing 1.0 by their squares. For example, wd = 1./numpy.power(`sx`, 2).

covx and covy are arrays of covariance matrices and are converted to weights by performing a matrix inversion on each observation’s covariance matrix. For example, we[i] = numpy.linalg.inv(covy[i]).

These arguments follow the same structured argument conventions as wd and we only restricted by their natures: sx and sy can’t be rank-3, but covx and covy can be.

Only set either sx or covx (not both). Setting both will raise an exception. Same with sy and covy.

Methods

set_meta(**kwds) Update the metadata dictionary with the keywords and data provided by keywords.