def METHOD_NAME(self):
    """
    Test generation of orientation angles.
    We expect long. and periapse to be uniformly distributed and
    inclination to be sinusoidally distributed.
    Edit made by Sonny Rappaport, Cornell, July 2021:
    SciPY update has broken this method, so use KS test to check inclination
    distribution and alter usage of chi^2 test for the uniform distributions
    """
    pp = PlanetPopulation(**self.spec)
    I, O, w = pp.gen_angles(self.nsamp)
    # O & w are expected to be uniform
    for j, (param, param_range) in enumerate(zip([O, w], [pp.Orange, pp.wrange])):
        pval = scipy.stats.kstest(
            param.value, scipy.stats.uniform.cdf, args=tuple(param_range.value)
        ).pvalue
        if pval < self.kscrit:
            _, param, param = pp.gen_angles(self.nsamp)
            pval = scipy.stats.kstest(
                param.value, scipy.stats.uniform.cdf, args=tuple(param_range.value)
            ).pvalue
        self.assertGreater(
            pval,
            self.kscrit,
            "{} does not appear uniform.".format(["Omega", "omega"][j]),
        )
    # cdf of the sin distribution for ks test
    sin_cdf = lambda x: (-np.cos(x) / 2 + 0.5)
    pval = scipy.stats.kstest(I, sin_cdf).pvalue
    # allowed one do-over for noise
    if pval <= self.kscrit:
        I, _, _ = pp.gen_angles(self.nsamp)
        pval = scipy.stats.kstest(I, sin_cdf).pvalue
    self.assertGreater(pval, self.kscrit, "I does not appear sinusoidal")