minisom python package pca initialization code

Question

Copied PCA initialization code from minisom package shows below.

    def pca_weights_init(self, data):
        """Initializes the weights to span the first two principal components.
        This initialization doesn't depend on random processes and
        makes the training process converge faster.
        It is strongly reccomended to normalize the data before initializing
        the weights and use the same normalization for the training data.
        """
        if self._input_len == 1:
            msg = 'The data needs at least 2 features for pca initialization'
            raise ValueError(msg)
        self._check_input_len(data)
        if len(self._neigx) == 1 or len(self._neigy) == 1:
            msg = 'PCA initialization inappropriate:' + \
                  'One of the dimensions of the map is 1.'
            warn(msg)
        pc_length, pc = linalg.eig(cov(transpose(data)))
        pc_order = argsort(-pc_length)
        for i, c1 in enumerate(linspace(-1, 1, len(self._neigx))):
            for j, c2 in enumerate(linspace(-1, 1, len(self._neigy))):
                self._weights[i, j] = c1*pc[pc_order[0]] + c2*pc[pc_order[1]]

Shouldn't the last line be changed to the following since eigenvectors are shown column-wise?

self._weights[i, j] = c1*pc[:,pc_order[0]] + c2*pc[:,pc_order[1]]