[BUG] - Complex Distributions (Mixture, Competing Risks, DSZI) quantile and inverse_SF functions error when input is array-like

[ctenold]

Describe the bug
quantile() and inverse_SF() functions work fine with a single numeric input, but error out when provided an array or list

To Reproduce

from reliability.Distributions import Mixture_Model, Weibull_Distribution

one = Weibull_Distribution(alpha=0.04, beta=2.2)
two = Weibull_Distribution(alpha=0.18, beta=1.3)
p = 0.43
mixture = Mixture_Model([one, two], [p, 1-p] )
mixture.quantile([.1, .2])

Returns: ValueError: operands could not be broadcast together with shapes (1000000,) (2,)

Additional context
Here's an example of how I fixed it:

• if q is a number, convert to list to avoid unpack_single_arrays() error
• Vectorize the ppf function

    def quantile(self, q):
        """
        Quantile calculator

        Parameters
        ----------
        q : float, list, array
            Quantile to be calculated. Must be between 0 and 1.

        Returns
        -------
        x : float
            The inverse of the CDF at q. This is the probability that a random
            variable from the distribution is < q
        """
        if type(q) in [int, float, np.float64]:
            if q < 0 or q > 1:
                raise ValueError("Quantile must be between 0 and 1")
            else:
                q=[q]
        elif type(q) in [list, np.ndarray]:
            if min(q) < 0 or max(q) > 1:
                raise ValueError("Quantile must be between 0 and 1")
        else:
            raise ValueError("Quantile must be of type float, list, array")
        # ppf = self.__xvals_init[np.argmin(abs(self.__cdf_init - q))]
        def mixture_ppf(q):
            return self.__xvals_init[np.argmin(abs(self.__cdf_init - q))]
        mixture_ppf_vec = np.vectorize(mixture_ppf)
        ppf = mixture_ppf_vec(q)
        return unpack_single_arrays(ppf)