This example generates a self consistent realization of an anisotropic Plummer model, by sampling the distribution function derived by (Dejonghe 1987). The parameters that control the ICs are essentially: * a: The Plummer softening length * M: The total mass of the system * q: A parameter that controls the radial anisotropy of the system, ranging from -inf to 2, where -inf corresponds to the "Einstein Sphere", a model with only circular orbits and 2 to a model with only radial orbits. q is related to the traditional beta parameter (which measures anisotropy) via beta(r) = q/2 * r^2/(1+r^2) * N: The Number of particles in the system Some additional parameters are: * bound: A given cut radius above which particles are not included in the IC file (this may reduce the effective number of particles to N_eff < N). N_eff is printed out when the script is executed. * use_parallel: Use python's multiprocessing library for sampling * nb_threads: Number of threads to use for the above * pickle_ics: Also output .pkl files of the pos, vel and mass arrays All other variables should be self-explanatory. The radii of the particles are renerated through inverse transform sampling, exploiting the analiticity of the inverse of M(