Example for creating numpy arrays of data

[greglucas]

The current examples describe how to get individual data items and inspect them, but I suspect most people are wanting to fill arrays or some other data structures from the data. On IMAP we have a utility function to handle creating xarray.Dataset objects for each apid. I suspect a utility function like this would be valuable to other missions as well, but I don't think we'd want to add large dependencies to the library. Maybe we could add optional dependencies hidden behind try/except ImportError blocks to support some of these utilities? Or the easiest would be to add examples of how to do this and let everyone copy/paste the examples.

Example code for creating a mapping of apid: Dataset
https://github.com/IMAP-Science-Operations-Center/imap_processing/blob/373f9f382260188d94366007853b673982ba1275/imap_processing/utils.py#L298-L332

[medley56]

As long as the code is compact enough (e.g. in its own module), hiding things behind import errors works for me. We can add the dependencies in an "extra" dependency set.

[medley56]

@greglucas Let's upstream your code from IMAP to support conversion to an xarray DataSet. I'd like to improve the handling of derived values if possible. I think for numeric values we can just default to float64 (Splines and Polynomials both return python floats) and for enums, we could use a Unicode String ("U") type or a Python Object "O" type. This isn't memory efficient but without knowing the full list of possible values a priori, I'm not sure how else we could approach it. We can also always include an option to only create arrays of raw values.