How to use the probscale.validate.fit_argument function in probscale

Returns
    -------
    xhat, yhat : numpy arrays
        Linear model estimates of ``x`` and ``y``.
    results : dict
        Dictionary of linear fit results. Keys include:

          - slope
          - intersept
          - yhat_lo (lower confidence interval of the estimated y-vals)
          - yhat_hi (upper confidence interval of the estimated y-vals)

    """

    fitprobs = validate.fit_argument(fitprobs, "fitprobs")
    fitlogs = validate.fit_argument(fitlogs, "fitlogs")

    # maybe set xhat to default values
    if xhat is None:
        xhat = copy.copy(x)

    # maybe set dist to default value
    if dist is None:
        dist = _minimal_norm

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

    # maybe compute ppf of y

Returns
    -------
    xhat, yhat : numpy arrays
        Linear model estimates of ``x`` and ``y``.
    results : dict
        Dictionary of linear fit results. Keys include:

          - slope
          - intersept
          - yhat_lo (lower confidence interval of the estimated y-vals)
          - yhat_hi (upper confidence interval of the estimated y-vals)

    """

    fitprobs = validate.fit_argument(fitprobs, "fitprobs")
    fitlogs = validate.fit_argument(fitlogs, "fitlogs")

    # maybe set xhat to default values
    if xhat is None:
        xhat = copy.copy(x)

    # maybe set dist to default value
    if dist is None:
        dist = _minimal_norm

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

    # maybe compute ppf of y
    if fitprobs in ['y', 'both']: