bw2calc.single_matrix

Attributes

PackagesDataLoader
pandas

Classes

SingleMatrixLCA

An LCA which puts everything into one matrix.

Module Contents

class bw2calc.single_matrix.SingleMatrixLCA(demand, data_filepath, log_config=None, presamples=None, seed=None, override_presamples_seed=False)

Bases: object

An LCA which puts everything into one matrix.

Comes with advantages and disadvantages, and designed exclusively for BONSAI via beebee.

Create a new single-matrix LCA calculation.

Parameters:

demand (*) – The demand or functional unit. Needs to be a dictionary to indicate amounts, e.g. {("my database", "my process"): 2.5}.

Returns:

A new SingleMatrixLCA object

build_demand_array(demand=None)

Turn the demand dictionary into a NumPy array of correct size.

Parameters:

demand (*) – Demand dictionary. Optional, defaults to self.demand.

Returns:

A 1-dimensional NumPy array

calculate(factorize=False, builder=SingleMatrixBuilder)

Calculate an LCA score.

Creates self.supply_array, a vector of activities, flows, and characterization pathways which satisfy the demand.

Creates self.scores, a dictionary of {'LCIA identifier': LCA score}.

Create self.contributions, a dictionary of {'LCIA identifier': []}.

Parameters:

• factorize (*) – Factorize the technosphere matrix. Makes additional calculations with the same technosphere matrix much faster. Default is False; not useful is only doing one LCI calculation.

• builder (*) – Custom matrix builders can be used to manipulate data in creative ways before building the matrices.

Doesn’t return anything.

calculate_scores()

decompose_technosphere()

Factorize the technosphere matrix into lower and upper triangular matrices, \(A=LU\). Does not solve the linear system \(Ax=B\).

Doesn’t return anything, but creates self.solver.

Warning

Incorrect results could occur if a technosphere matrix was factorized, and then a new technosphere matrix was constructed, as self.solver would still be the factorized older technosphere matrix. You are responsible for deleting self.solver when doing these types of advanced calculations.

fix_dictionaries(row_mapping, col_mapping)

Fix the row and column dictionaries from {integer: row/col index} to {label: row/col index}.

lcia(*args, **kwargs)

load_beebee_data(builder=SingleMatrixBuilder)

Load beebee export data package.

This is a compressed file which contains:

• A stats_arrays file used to create the single matrix.

• A mapping dictionary from meaningful labels to the integer row ids

• A mapping dictionary from meaningful labels to the integer column ids

• A mapping dictionary from {"method URI": {labels}} which allows for LCIA sums

rebuild_matrix(vector)

Build a new technosphere matrix using the same row and column indices, but different values. Useful for Monte Carlo iteration or sensitivity analysis.

Parameters:

vector (*) – 1-dimensional NumPy array with length (# of technosphere parameters), in same order as self.tech_params.

Doesn’t return anything, but overwrites self.technosphere_matrix.

redo_calculate(demand=None)

Redo LCI with same databases but different demand.

Parameters:

demand (*) – A demand dictionary.

Doesn’t return anything, but overwrites self.demand_array, self.supply_array, and self.inventory.

Warning

If you want to redo the LCIA as well, use redo_lcia(demand) directly.

reverse_dict()

Construct reverse dicts from technosphere and biosphere row and col indices to activity_dict/product_dict/biosphere_dict keys.

Returns:

(reversed self.activity_dict, self.product_dict and self.biosphere_dict)

solve_linear_system()

Master solution function for linear system \(Ax=B\).

To most numerical analysts, matrix inversion is a sin.

—Nicolas Higham, Accuracy and Stability of Numerical Algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2002, p. 260.

We use UMFpack, which is a very fast solver for sparse matrices.

If the technosphere matrix has already been factorized, then the decomposed technosphere (self.solver) is reused. Otherwise the calculation is redone completely.

demand

filepath

logger

seed

bw2calc.single_matrix.PackagesDataLoader = None

bw2calc.single_matrix.pandas = None