Copasi-basico

This release adds support for the MCA task to basico, for an example on how to use it see:

* [MCA Example](https://basico.readthedocs.io/en/latest/notebooks/MCA.html)

Basico is a simplified python interface to COPASI. While all functionality from COPASI is exposed via automatically generated SWIG wrappers, this package aims to add a layer on top of that, to hide most of the complexity away when calling COPASI functions.

This release adds support for using constraints when running optimization or parameter estimation tasks. They do make things much more difficult to solve, so use with care. I've updated the examples to show how to use them:

* [Parameter estimation example](https://basico.readthedocs.io/en/latest/notebooks/Setting_up_Parameter_Estimation.html#Constraints)
* [Optimization example](https://basico.readthedocs.io/en/latest/notebooks/Optimization.html#Constraints)

**Full Changelog**: https://github.com/copasi/basico/compare/v0.51...v0.52

This release introduces a new method `run_scheduled_tasks` and the general `run_task` command, that executes tasks, and aims to produce the output specified. For now basic plotting is implemented, but will be refined in the next versions.

It also includes a number of bugfixes (fixes 34, 35 , 36), thanks to ayush9pandey for reporting. This should produce more applicable error messages when running simulations, sort an issue where Miriam information could not be retrieved when using python-copasi > 4.35 and clarify issues in the notebooks.

**Full Changelog**: https://github.com/copasi/basico/compare/v0.50...v0.51

This release upgrades the support for petab_select, cleans up logging (not using the root logger anymore), and also supports observable transformations for PEtab problems. Additionally it exposes the Hybrid Runge Kutta method (fixes 33 ) to the `run_time_course` / `run_time_course_with_output` functions.

Basico is a simplified python interface to COPASI. While all functionality from COPASI is exposed via automatically generated SWIG wrappers, this package aims to add a layer on top of that, to hide most of the complexity away when calling COPASI functions.

New in this release is support for advanced event flags, such as:

* `fire_at_initial_time`: to indicate that an event should trigger at time = t0, (default False)
* `persistent`: to indicate that an event has to remain true between trigger time and delay (default False)
* `delay_calculation`: to indicate that not just the assignment, but also the calculation of the assignment expression should be delayed
* `priority`: a priority expression that decides which event will get priority if multiple ones trigger at the same time (default random)

Basico is a simplified python interface to COPASI. While all functionality from COPASI is exposed via automatically generated SWIG wrappers, this package aims to add a layer on top of that, to hide most of the complexity away when calling COPASI functions.

New in this release is the plotting for profile likelihood profiles. Have a look at the [updated example](https://basico.readthedocs.io/en/latest/notebooks/Profile_likelihood.html), by default we draw the 68% and 95% threshold.