def unconstrain_stationary_multivariate(constrained, error_variance):

    from scipy import linalg

    use_list = type(constrained) == list
    if not use_list:
        k_endog, order = constrained.shape
        order //= k_endog

        constrained = [
            constrained[:k_endog, i*k_endog:(i+1)*k_endog]
            for i in range(order)
        ]
    else:
        order = len(constrained)
        k_endog = constrained[0].shape[0]

    # Step 1: convert matrices from the space of stationary
    # coefficient matrices to our "partial autocorrelation matrix" space
    # (matrices with singular values less than one)
    partial_autocorrelations = _compute_multivariate_pacf_from_coefficients(
        constrained, error_variance, order, k_endog)

    # Step 2: convert from arbitrary matrices to those with singular values
    # less than one.
    unconstrained = _unconstrain_sv_less_than_one(
        partial_autocorrelations, order, k_endog)

    if not use_list:
        unconstrained = np.concatenate(unconstrained, axis=1)

    return unconstrained, error_variance
Push some new data into this object.
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/statespace/tools.py/unconstrain_stationary_multivariate--crossover with--dataset/ETHPy150Open babble/babble/include/jython/Lib/email/feedparser.py/BufferedSubFile.push