def METHOD_NAME(logps, molecule_name, scheme, component):
    """
    Create line plot of mean and standard deviation of given logPs.
    Parameters
    ----------
        logps: dict { int : np.ndarray }
            key : number of total NCMC steps
            value : array of `niterations` logP values
        molecule_name : str
            The molecule featured in the NullTestSystem being analyzed
            in ['naphthalene','butane','propane']
        scheme : str
            Which NCMC scheme is being used
            in ['hybrid','two-stage']
        component : str
            Which logP is being plotted
            in ['NCMC','EXEN']
    """
    x = list(logps.keys())
    x.sort()
    y = [logps[steps].mean() for steps in x]
    dy = [logps[steps].std() for steps in x]
    plt.fill_between(x, [mean - dev for mean, dev in zip(y, dy)], [mean + dev for mean, dev in zip(y, dy)])
    plt.plot(x, y, 'k')
    plt.xscale('log')
    plt.title("{0} {1} {2} {3}".format(ENV, molecule_name, scheme, component))
    plt.ylabel('logP')
    plt.xlabel('ncmc steps')
    plt.tight_layout()
    plt.savefig('{0}_{1}_{2}{3}_logP'.format(ENV, molecule_name, scheme, component))
    print('Saved plot to {0}_{1}_{2}{3}_logP.png'.format(ENV, molecule_name, scheme, component))
    plt.clf()