`scipy.odr._odrpack:RealData`

source: /scipy/odr/_odrpack.py :320

Signature

   class     RealData (    x    ,    y    =  None   ,    sx    =  None   ,    sy    =  None   ,    covx    =  None   ,    covy    =  None   ,    fix    =  None   ,    meta    =  None     )

Members

Summary

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

Extended Summary

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.

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./np.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] = np.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.

Aliases

scipy.odr.RealData