(pfm, n_occur)
return approx_error
Calculate approximate error for small count motif information content

    Parameters
    ----------
    pfm: dict
        {'A': [0.1,0.3,0.2], 'T':[0.3,0.1,0.2], 'G': [0.1,0.3,0.3], 'C':[0.5,0.3,0.3]}
    n: int
        Number of sites

    Returns
    -------
    approx_error: float
        Approx error
Calculate approximate error for small count motif information content
Calculate approximate error for small count motif information content
def approximate_error(pfm, n_occur):
    """Calculate approximate error for small count motif information content

    Parameters
    ----------
    pfm: dict
        {'A': [0.1,0.3,0.2], 'T':[0.3,0.1,0.2], 'G': [0.1,0.3,0.3], 'C':[0.5,0.3,0.3]}
    n: int
        Number of sites

    Returns
    -------
    approx_error: float
        Approx error


    """

    bases = list(pfm.keys())
    approx_error = (len(bases) - 1) / (2 * np.log(2) * n_occur)
    #print(approx_error)
    return approx_error
def approximate_error ( pfm , n_occur ) : bases = list ( pfm . keys ( ) ) approx_error = ( len ( bases ) - 1 ) / ( 2 * np . log ( 2 ) * n_occur ) #print(approx_error) return approx_error