# dynamic_characterization

This is a package for the dynamic characterization of Life Cycle Inventories with temporal information. It includes a collection of dynamic characterization functions for various environmental flows. We also provide a simple interface to apply these functions to an existing dynamic LCI (coming from, e.g., [bw_temporalis](https://github.com/brightway-lca/bw_temporalis) or [bw_timex](https://github.com/brightway-lca/bw_timex)).

The following dynamic characterization functions are currently included:

| module |impact category | metric | covered emissions | source
|--------|-------|----------|----------|--|
| ipcc_ar6 | climate change | radiative forcing | 247 GHGs | radiative efficiencies & lifetimes from [IPCC AR6 Ch.7](https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7/) |
| original_temporalis_functions| climate change | radiative forcing | CO2, CH4 |[bw_temporalis](https://github.com/brightway-lca/bw_temporalis/tree/main)|

## What do dynamic characterization functions do?

The functions are meant to work with a common input format of the dynamic inventory, collected in a pandas DataFrame that looks like this:

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

Each function takes one row of this dynamic inventory dataframe (i.e. one emission at one point in time) and transform it according to some metric. The output generated by applying a very simple function to both rows of the input dataframe could look like:

| date | amount | flow | activity |
|------|--------|------|----------|
| 101  | 33     | 1    | 2        |
| 102  | 31     | 1    | 2        |
| 103  | 31     | 1    | 2        |
| 312  | 21     | 4    | 2        |
| 313  | 20     | 4    | 2        |
| 314  | 19     | 4    | 2        |


## What do dynamic characterization functions look like?

Here's an example of what such a function could look like:

```python
def example_characterization_function(series: namedtuple, period: int = 2) -> namedtuple:
    date_beginning: np.datetime64 = series.date.to_numpy()
    dates_characterized: np.ndarray = date_beginning + np.arange(
        start=0, stop=period, dtype="timedelta64[D]"
    ).astype("timedelta64[s]")

    amount_beginning: float = series.amount

    # in reality, this would probably something more complex like an exponential decay function
    amount_characterized: np.ndarray = amount_beginning - np.arange(
        start=0, stop=period, dtype="int"
    )

    return namedtuple("CharacterizedRow", ["date", "amount", "flow", "activity"])(
        date=np.array(dates_characterized, dtype="datetime64[s]"),
        amount=amount_characterized,
        flow=series.flow,
        activity=series.activity,
    )
```

## Support

If you have any questions or need help, do not hesitate to contact Timo Diepers ([timo.diepers@ltt.rwth-aachen.de](mailto:timo.diepers@ltt.rwth-aachen.de))

```{toctree}
---
hidden:
maxdepth: 1
---
content/usage
content/api/index
Code of Conduct <content/codeofconduct>
Contributing <content/contributing>
content/license
Changelog <content/changelog>
```