How does your dynamic inventory look like?

We aim to make `dynamic_characterization` useful for everybody, regardless of their preferred LCA software.

As enthusiastic Brightway users, we have defined a data format like this:

| date | amount | flow | activity |
|-------|-------|------|----------|
| 101 | 33 | 1 | 2 |
| 312 | 21 | 4 | 2 |

where date is a timestamp, e.g. a `datetime`-object, amount is the amount of the biosphere flow, flow is the flow id, e.g. a integer, and activity is the activity id, also an integer. Then, each row of this dataframe gets characterized by `dynamic_characterization.characterize()`

This format works well with Brightway, but we are curious about the needs of the wider community.

What does your dynamic inventory look like and what kind of challenges do you face during dynamic characterization that could be addressed in this package?

Please feel free to use this issue to share your dataformat, needs or any suggestions to make `dynamic_characterization` more useful to you.

Start with the Brightway-oriented inventory table in the issue and the dynamic_characterization.characterize() entry point. No file or test is named; gather the community's inventory formats and characterization needs, then define and document an agreed format or requirements for broader use.

Written by the indexing model from the issue text.