Generic import/export function for Obs and ObsCollection classes about hydropandas

[dbrakenhoff]

You can pickle the ObsCollection/Observations using the to_pickle() method. Loading these using
pandas (pd.read_pickle()) will give you back the original ObsCollection or Observation. Maybe that solves your issue somewhat?

I'm all for a generic human-readable export format for Observations. I'd suggest some kind of CSV format that includes some information about its Obs type(?). Then I guess we need to the define some kind of header format and then write the time series data below that. If we want to attempt to maintain data types on import, that will be a bit of a challenge. An ObsCollection could then just use that Observation export format to write CSV files for each Observation in the collection.

If anyone else has any suggestions regarding this topic, feel free to post them here.

[tdmeij]

Thank you David, this answers my question. After reading back the pickled object, I even get an ObsCollection object instead of the DataFrame I had expected. Magic still happens, apparently.

[OnnoEbbens]

Hahaha, I had the same first reaction when the pd.read_pickle() returned an ObsCollection object. It is magic!

There is also a to_excel() method for an ObsCollection. This will create an excel file with one tab with all the metadata and another tab for each observation object with the measurement time series. This is imo the best way to export to a human-readable format. Unfortunately we don't have a read_excel() method yet for an ObsCollection but I think it is not too hard to create one.

[martinvonk]

Maybe we can create a simple hpd.ObsCollection.from_pickle() method that calls pandas.read_pickle()? To increase findability.

[OnnoEbbens]

I've added a read_excel and read_pickle function to hydropandas. I updated the example notebook 01_groundwater_observations with calls to the excel and pickle functions for an ObsCollections.