Storing oseries to models relationship

[dbrakenhoff]

The latest PR #49 adds functionality to keep track of models per oseries. This is useful to keep track of, e.g. for getting a list of models for a certain location. The downside of the current implementation is that it requires a run through all stored models to build this dictionary, which can take a few seconds when creating a Connector object linking to an existing database.

This issue is a reminder to maybe think about a faster more efficient way to keep track of this, i.e. store this relationship in a separate library that is updated with each add_model() and del_model() call. This avoids having to rebuild this dictionary each time you connect to the database. Or perhaps another solution...?

[dbrakenhoff]

New proposed solution to this problem. Still a bit of a work in progress and I'm not sure whether this is the way to go yet.

Upsides:

• Oseries to models link is stored, so no need to reconstruct this relation on load.
• See list of linked models directly in oseries DataFrame.
• List of models stored directly in oseries metadata.
• Simple implementation using existing libraries
• Get dictionary of {oseries: [model_names_list]} through pstore.oseries_models property

There are some performance downsides to this implementation, but I'm not sure if they're really noticeable in practice...

Downsides:

• The oseries cache is cleared every time a model is added or deleted and will have to be reconstructed after that.
• Updating the oseries metadata to add a model link requires reading and then writing the oseries timeseries+metadata each time. This means 1 extra read/write for each model added/deleted.
• Deleting and then adding an oseries again will remove the model_links entry. Currently there is no logic to automatically rebuild this model_links entry in this case.

[dbrakenhoff]

A different proposed solution is presented in #68. This implementation creates a new library oseries_models where the relationship between models and oseries will be stored.

Upsides:

• Names of models for a certain oseries are stored, making it easy to obtain models for a specific point without having to recalculate that relationship every time.
• Relative to previous implementation, much more efficient. No need to read/write timeseries/metadata. Only has to store a single list with model names every time a model is added/deleted. No need to clear cached oseries dataframe after every model add/delete.
• Get dictionary of {oseries: [model_names_list]} through pstore.oseries_models property

Downsides:

• links between oseries and models not stored in oseries metadata DataFrame (but it can be easily obtained from pstore.oseries_models).
• Added library added some complexity but not all that much.

[dbrakenhoff]

Added in #62, closing issue.