Anticpy

Add a further print output correction.

Two minor adaptions to the print output are included:

1. The completion_control value which is used to control the correct termination of cp_scan(...), fit(...), and compute_CP_pdfs(...) is reset to zero in compute_CP_pdfs(...) to avoid wrong outputs if one of the former methods is used before the latter.
2. The print output of the batched CP fit is corrected for the singular or plural of "round".

This post-release is related to the computation of the marginal CP PDFs. For a high number of CP configurations, the multiprocessing pool allocates excessive amounts of memory to prepare all jobs. Therefore, the option

- `queue_managment` is added which allows for opening and closing of multiprocessing pools per batch. This resolves the memory issue and guarantees feasible computation times.

**Adaptations:**

1. Remove window size output for MAP estimation procedures.
2. Adapt the variable name batchsize in batched_configs(...) to batch_size for congruence.

**Bug Fixes:**

1. Correct multiprocessing options of the emcee MCMC sampling in perform_resilience_scan(...).
2. Correct normalization of the marginal CP PDFs in compute_CP_pdfs(...). The marginalized PDFs now individually sum up to one.

v.0.0.8.post1
**Minor Enhancements:**

- The _window_shift_ option of the _RocketFastResilienceScan_ wrapper is enhanced. Instead of the integer value for an equidistant window shift which is already known from _LangevinEstimation_ and _NonMarkovEstimation_, the parameter can be defined as one dimensional numpy array of integers. The entries correspond to specific window shifts that are executed by the workers before computing the corresponding windows. This can help to easily fill up missing values of a first parallel calculation or to pick out specific windows of interest in general.

**Bug Corrections:**

- After the last release, the _nburn_ parameter passing was corrected for _LangevinEstimation_ and _NonMarkovEstimation_. Some bug remained for the _LangevinEstimation_ class. It is corrected in this post release.

Two bugs are fixed:

1. A wrong version of the dominant_eigenvalue.false_NN routine was uploaded. The algorithm is corrected so far.
2. Some parameter passing issues for drift_slope.LangevinEstimation and drift_slope.NonMarkovEstimation are solved.

Since the current matplotlib version reorganized the collection objects already some time ago, the code to create animations with the LangevinEstimation is updated in course of this post-release. In principle, there are no changes to the package's usability, functions and algorithms. Only the animation is updated for those who like to create animations with the current matplotlib version. Therefore, the release is ranked to be a post-release.