d1d70f4790700927e289a3702b17fbde8f1b8631,scanpy/preprocessing/_utils.py,,_pca_with_sparse,#Any#Any#Any#Any#Any#,21

Before Change

    u = u[:, idx]
    v = v[idx, :]
    u, v = svd_flip(u, v)
    return u * s, v


def sparse_mean_variance_axis(mtx: sparse.spmatrix, axis: int):
    

After Change

    v = v[idx, :]

    X_pca = (u * s)[:, idx]
    ev = X_pca.var(0)

    total_var = _get_mean_var(X)[1].sum()
    ev_ratio = ev / total_var

    output = {
        "X_pca": X_pca,
        "variance": ev,
        "variance_ratio": ev_ratio,
        "components": v,
    }
    return output

def sparse_mean_variance_axis(mtx: sparse.spmatrix, axis: int):
    

Instances