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scipy.odr.

# RealData

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

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

  Parameters:
    xarray_like

      Observed data for the independent variable of the regression

    yarray_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.

    sxarray_like, optional

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

    syarray_like, optional

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

    covxarray_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.

    covyarray_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.

    fixarray_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.

    metadict, optional

      Free-form dictionary for metadata.

  Methods

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

  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.