Copasi-basico

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.

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 capability to create / edit / update / remove model parameter sets. Have a look at the tutorial:

* <https://basico.readthedocs.io/en/latest/notebooks/Working_with_ParameterSets.html>

This release also fixes some more issue, when using relative filenames for experimental data.

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:

* fixes an issue with setting up timecourses, where inputs are now automatically converted to the types needed by the C++ api. This allows duration to work also with an np.float value
* `save_experiments_to_dict`, `save_model_and_data` now use relative filenames for the experiment files. So the files will work readily after copying them to other environments.