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The various stochastic Monte Carlo LCA classes function almost the same
as the static LCA, and reuse most of the code. The only change is that
instead of building matrices once, random number generators from
are instantiated directly from each parameter array. For each Monte
Carlo iteration, the
amount column is then overwritten with the output
from the random number generator, and the system solved as normal. The
code to do a new Monte Carlo iteration is quite succinct:
def next(self): self.rebuild_technosphere_matrix(self.tech_rng.next()) self.rebuild_biosphere_matrix(self.bio_rng.next()) if self.lcia: self.rebuild_characterization_matrix(self.cf_rng.next()) self.lci_calculation() if self.lcia: self.lcia_calculation() return self.score else: return self.supply_array
This design is one of the most elegant parts of Brightway2.
Because there is a common procedure to build static and stochastic matrices, any matrix can easily support uncertainty, e.g. not just LCIA characterization factors, but also weighting, normalization, and anything else you can think of; see Defining a new Matrix - example of Weighting and Normalization matrices.