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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def value(self):
 """return float Cumulative Distribution Function. The return value represents a floating point number of the CDF of the largest eigenvalue of a Wishart(n, p) evaluated at chisq_val. """

        wishart = self._wishart_cdf

        # Prepare variables for integration algorithm
        A = self.A
        p = self._gammainc_a
        g = gamma(wishart.alpha_vec)
        q_ind = np.arange(2 * wishart.n_min - 2)
        q_vec = 2 * wishart.alpha + q_ind + 2
        q = np.float_power(0.5, q_vec) * gamma(q_vec) * gammainc(q_vec, self._chisq_val)

        # Perform integration (i.e. calculate Pfaffian CDF)
        for i in xrange(wishart.n_min):
            # TODO consider index tricks instead of iteration here
            b = 0.5 * p[i] * p[i]
            for j in xrange(i, wishart.n_min - 1):
                b -= q[i + j] / (g[i] * g[j + 1])
                A[j + 1, i] = p[i] * p[j + 1] - 2 * b
                A[i, j + 1] = -A[j + 1, i]

        if np.any(np.isnan(A)):
            return 0
        return np.sqrt(det(A))