SciPy

numpy.ma.cov

numpy.ma.cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None)[source]

Estimate the covariance matrix.

Except for the handling of missing data this function does the same as numpy.cov. For more details and examples, see numpy.cov.

By default, masked values are recognized as such. If x and y have the same shape, a common mask is allocated: if x[i,j] is masked, then y[i,j] will also be masked. Setting allow_masked to False will raise an exception if values are missing in either of the input arrays.

Parameters :

x : array_like

A 1-D or 2-D array containing multiple variables and observations. Each row of x represents a variable, and each column a single observation of all those variables. Also see rowvar below.

y : array_like, optional

An additional set of variables and observations. y has the same form as x.

rowvar : bool, optional

If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.

bias : bool, optional

Default normalization (False) is by (N-1), where N is the number of observations given (unbiased estimate). If bias is True, then normalization is by N. This keyword can be overridden by the keyword ddof in numpy versions >= 1.5.

allow_masked : bool, optional

If True, masked values are propagated pair-wise: if a value is masked in x, the corresponding value is masked in y. If False, raises a ValueError exception when some values are missing.

ddof : {None, int}, optional

New in version 1.5.

If not None normalization is by (N - ddof), where N is the number of observations; this overrides the value implied by bias. The default value is None.

Raises :

ValueError :

Raised if some values are missing and allow_masked is False.

See also

numpy.cov

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