bw2landbalancer 0.1

Used to create balanced LCA land transformation exchange samples to override unbalanced samples in Brightway2.

bw2landbalancer

bw2landbalancer is a Python library used to create balanced land transformation samples to override unbalanced sample.

Unbalanced samples arise when land transformation exchanges are independently sampled. bw2landbalancer rescales certain land transformation exchanges to ensure that the ratio of land transformation from exchanges and land transformation to exchanges is conserved. It is based on the Brightway2 LCA framework, and is meant to be used with presamples.

It was developped with ecoinvent in mind, though the modifications required to make it useful for other databases would be minimal. Currently, elementary flows (biosphere exchanges in Brightway2) are identified as land transformation inputs or outputs based on the patterns "Transformation, from" and "Transformation, to", respectively. Other patterns can be added.

Installation

Use the package manager pip to install bw2landbalancer:

pip install bw2landbalancer

or use conda:

conda install --channel pascallesage bw2landbalancer

Usage

from bw2landbalancer import DatabaseLandBalancer
from brightway2 import projects

projects.set_current("my project")

dlb = DatabaseLandBalancer(
    database_name="ecoinvent_cutoff", #name the LCI db was given on import
)

Note: This is where one could add additional patters to identify land transformation exchanges, via the land_from_patterns and land_to_patterns (lists of strings).

# Generate samples, and format as matrix_data for use in presamples
dlb.add_samples_for_all_acts(iterations=1000)

0% [##############################] 100% | ETA: 00:00:00 Total time elapsed: 00:18:11

# Create presamples package
dlb.create_presamples(
    name='some name', 
    dirpath=some/path, 
    id_='some id',
    seed='sequential', #or None, or int
    )

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Acknowledgment

Special thanks to Quantis US for having funded the early iterations of this work.