single_inversion generates a starting point, x0, from a multivariate_normal distribution and runs the inversion twice (calling tip_single_inversion) to get the best solution,
however;
tip_single_inversion immediately overwrites x0 with mu so the processing in single_inversion is for nothing. (and the x0 argument to tip_single_inversion is unnecessary).
Not a bug as such and not expecting a fix but noting it as an issue just to keep a record of it.
in tip_inversion.py:
single_inversion generates a starting point, x0, from a multivariate_normal distribution and runs the inversion twice (calling tip_single_inversion) to get the best solution, however; tip_single_inversion immediately overwrites x0 with mu so the processing in single_inversion is for nothing. (and the x0 argument to tip_single_inversion is unnecessary).
Not a bug as such and not expecting a fix but noting it as an issue just to keep a record of it.