bw_graph_tools.graph_traversal.utils

Classes

CachingSolver	Class which caches expensive linear algebra solution vectors.
Counter	Custom counter to have easy access to current value

Functions

get_demand_vector_for_activity(node, skip_coproducts, ...)

Get input matrix indices and amounts for a given activity. Ignores the reference production

Module Contents

class bw_graph_tools.graph_traversal.utils.CachingSolver(lca: bw2calc.LCA)

Class which caches expensive linear algebra solution vectors.

_calculate(index: int) → numpy.ndarray

add_to_cache(index: int, result: numpy.ndarray) → None

Store a pre-computed supply vector. Result must be for a demand amount of 1.

calculate(index: int) → numpy.ndarray

Compute cumulative LCA score for one unit of a given activity.

in_cache(indices: set[int]) → set[int]

Return all indices values which are in the cache.

_cache

lca

class bw_graph_tools.graph_traversal.utils.Counter

Custom counter to have easy access to current value

value = -1

bw_graph_tools.graph_traversal.utils.get_demand_vector_for_activity(node: bw_graph_tools.graph_traversal.graph_objects.Node, skip_coproducts: bool, matrix: scipy.sparse.spmatrix)

Get input matrix indices and amounts for a given activity. Ignores the reference production exchanges and optionally other co-production exchanges.

Parameters:

• node (Node) – Activity whose inputs we are iterating over

• skip_coproducts (bool) – Whether or not to ignore positive production exchanges other than the reference product, which is always ignored

• matrix (scipy.sparse.spmatrix) – Technosphere matrix

Returns:

• row indices (list) – Integer row indices for products consumed by Node

• amounts (list) – The amount of each product consumed, scaled to Node.supply_amount. Same order as row indices.