geocat.comp.eofunc

geocat.comp.eofunc(data: Iterable, neval, **kwargs) → <MagicMock id='140459842795408'>

Computes empirical orthogonal functions (EOFs, aka: Principal Component Analysis).

Parameters

• data – an iterable object containing numbers. It must be at least a 2-dimensional array. The right-most dimension is assumed to be the number of observations. Generally this is the time time dimension. If your right-most dimension is not time, you could pass time_dim=x as an argument to define which dimension must be treated as time and/or number of observations. Data must be convertible to numpy.array

• neval – A scalar integer that specifies the number of eigenvalues and eigenvectors to be returned. This is usually less than or equal to the minimum number of observations or number of variables.

• **kwargs –

  extra options controlling the behavior of the function. Currently the following are supported: - jopt: a string that indicates whether to use the covariance matrix or the correlation

  matrix. The default is to use the covariance matrix.

  • pcrit: a float value between 0 and 100 that indicates the percentage of non-missing points

    that must exist at any single point in order to be calculated. The default is 50%. Points that contain all missing values will automatically be set to missing.

  • ’’time_dim``: an integer defining the time dimension. it must be between 0 and data.ndim - 1 or it

    could be -1 indicating the last dimension. The default value is -1.

  • missing_value: a value defining the missing value. The default is np.nan.

  • meta: If set to True and the input array is an Xarray, the metadata from the input array will be

    copied to the output array; default is False.