Classe « RealData »

Informations générales

Héritage

builtins.object
    Data
        RealData

Définition

class RealData(Data):

help(RealData)

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

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`.