A framework for distributed computation of LCA results.
- Antelope Interface Documentation
- Antelope Core Implementation Documentation
- User Guides and Demos, including support (via issues and [gitter])
The minimal installation includes only a few upstream dependencies so you can perform LCA without downloading very much (no scipy, pandas, or matplotlib, for instance).
$ python -m venv my_new_env
$ source my_new_env/bin/activate
(my_new_env) $ pip install antelope-core
(my_new_env) $ ipython>>> from antelope_core import LcCatalog
>>> cat = LcCatalog()
>>> cat.blackbook_guest('https://sc.vault.lc')
>>> This is sufficient for you to obtain data from a remote server and perform LCI/A operations and benchmarking. However, for modeling, you
will need antelope-foreground; for performing LCI and partial ordering you will need antelope-background which adds back in scipy,
(you may also want lxml for loading Ecospold or ILCD files); and for charts and reporting, you will need antelope-reports (which brings back
pandas and matplotlib, among others).
For more information on different installed configurations, check out [TBD]
Antelope is based on the concept that different kinds of information in LCA are needed for different purposes, and that storing and managing that information doesn't need to happen in one place or all at once. Antelope describes LCA computation in terms of different interfaces:
| Interface | Type of Information | Example | Values | Users may update |
|---|---|---|---|---|
| basic | Documentary / Metadata / LCIA Indicators | EPDs | Indicator Values | Docs + Metadata |
| exchange | Activity Descriptions and direct requirements | Unit Process Dataset | Reference Exchange Values | Exchanges and Exchange values |
| background | Economic / market models | LCI dataset | LCI data values | allocation and linking |
| quantity | Flow properties and characterization | LCIA Methodology | Characterization Factors | NA |
| foreground | Product System Models, Relationships, Scenarios | Suppliers and customers | Dynamic exchange values | observations |
These interfaces can be implemented by different services, independently of one another. Moreover, data owners can control which parts of these interfaces can be accessed by whom.
One key challenge Antelope was designed to address is the lack of "in-between" options for privacy. The most promising options for sharing LCI data at present involve contributing the dataset in its entirety to a third-party database, like ecoinvent or the Federal LCA Commons. When a study author does this, they lose control over their content. In the Antelope framework, a data owner can publish a study themselves, and control what aspects of the above interfaces different users may access. This is achieved by publishing a link to the study on an authentication server, and then issuing grants to individual users that permit them various shades of access.
Use Antelope software to host a data-free model to describe your study and system boundary, and disclose limited inventory and impact information to selected users while concealing it from others. The use of a shared model can turbocharge efforts at multi-party verification of studies and analytic systems.
All the core tools for antelope.py are open source and available on the Python Package Index.
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