How to use the probscale.algo function in probscale

# maybe compute ppf of y
    if fitprobs in ['y', 'both']:
        y = dist.ppf(y / 100.)

    # maybe compute log of x
    if fitlogs in ['x', 'both']:
        x = numpy.log(x)

    # maybe compute log of y
    if fitlogs in ['y', 'both']:
        y = numpy.log(y)

    yhat, results = algo._fit_simple(x, y, xhat, fitlogs=fitlogs)

    if estimate_ci:
        yhat_lo, yhat_hi = algo._bs_fit(x, y, xhat, fitlogs=fitlogs,
                                        niter=niter, alpha=alpha)
    else:
        yhat_lo, yhat_hi = None, None

    # maybe undo the ppf transform
    if fitprobs in ['y', 'both']:
        yhat = 100. * dist.cdf(yhat)
        if yhat_lo is not None:
            yhat_lo = 100. * dist.cdf(yhat_lo)
            yhat_hi = 100. * dist.cdf(yhat_hi)

    # maybe undo ppf transform
    if fitprobs in ['x', 'both']:
        xhat = 100. * dist.cdf(xhat)

    results['yhat_lo'] = yhat_lo

x = dist.ppf(x / 100.)
        xhat = dist.ppf(numpy.array(xhat) / 100.)

    # maybe compute ppf of y
    if fitprobs in ['y', 'both']:
        y = dist.ppf(y / 100.)

    # maybe compute log of x
    if fitlogs in ['x', 'both']:
        x = numpy.log(x)

    # maybe compute log of y
    if fitlogs in ['y', 'both']:
        y = numpy.log(y)

    yhat, results = algo._fit_simple(x, y, xhat, fitlogs=fitlogs)

    if estimate_ci:
        yhat_lo, yhat_hi = algo._bs_fit(x, y, xhat, fitlogs=fitlogs,
                                        niter=niter, alpha=alpha)
    else:
        yhat_lo, yhat_hi = None, None

    # maybe undo the ppf transform
    if fitprobs in ['y', 'both']:
        yhat = 100. * dist.cdf(yhat)
        if yhat_lo is not None:
            yhat_lo = 100. * dist.cdf(yhat_lo)
            yhat_hi = 100. * dist.cdf(yhat_hi)

    # maybe undo ppf transform
    if fitprobs in ['x', 'both']: