bw2waterbalancer
is a Python library used to create balanced water samples to override unbalanced sample.
Unbalanced samples arise when water exchanges are independently sampled. bw2waterbalancer
rescales certain exchanges
to ensure that the ratio of water inputs to water outputs is conserved. It is based on the
Brightway2 LCA framework, and is meant to be used with
presamples.
The rescaling is done according to three different strategy, depending on the nature of the exchanges:
* default: rescale sampled inputs so that mass ratio of sampled input to sampled output is same as
in static activity
* inverse: rescale sampled outputs so that mass ratio of sampled input to sampled output is same
as in static activity
* set_static: replace sampled exchanges with value in static activity
It was developped with ecoinvent in mind, though the modifications required to make it useful for other databases would be minimal.
Use the package manager pip to install bw2waterbalancer:
pip install bw2waterbalancer
or use conda:
conda install --channel pascallesage bw2waterbalancer
from bw2waterbalancer import DatabaseWaterBalancer
from brightway2 import projects
projects.set_current("my project")
dwb = DatabaseWaterBalancer(
ecoinvent_version="3.6", # used to identify activities with water production exchanges
database_name="ei36_cutoff", #name the LCI db in the brightway2 project
)
Validating data Getting information on technosphere water exchanges 0% [##############################] 100% | ETA: 00:00:00 Total time elapsed: 00:00:00 Getting information on biosphere water exchanges 0% [##############################] 100% | ETA: 00:00:00 Total time elapsed: 00:01:09
# Generate samples, and format as matrix_data for use in presamples
dwb.add_samples_for_all_acts(iterations=1000)
0% [##############################] 100% | ETA: 00:00:00 Total time elapsed: 00:18:11
# Create presamples package
dwb.create_presamples(
name='some name',
dirpath=some/path,
id_='some id',
seed='sequential', #or None, or int
)
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.
Special thanks to Quantis US for having funded the early iterations of this work.