import numpy as np
from bw2data import methods
from scipy.sparse import diags
from .base_class import RegionalizationBase
[docs]
class TwoSpatialScalesLCA(RegionalizationBase):
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matrix_labels = [
"biosphere_mm",
"geo_transform_mm",
"inv_mapping_mm",
"reg_cf_mm",
"technosphere_mm",
]
def __init__(self, *args, **kwargs):
r"""Perform regionalized LCA calculation, matching the spatial scales of inventory and impact assessment.
The calculation formula is:
.. math::
h_{r} = \left[ \textbf{MNGR} \right]^{T} \circ [ \textbf{B} \cdot (\textbf{A}^{-1}f) ]
Uses sparse matrix `elementwise multiplication <http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.multiply.html>`_.
In addition to the normal LCA initialization steps, this class does the following:
* Make sure that each inventory database has a set of ``geocollections`` in its metadata.
"""
super(TwoSpatialScalesLCA, self).__init__(*args, **kwargs)
if self.method not in methods:
raise ValueError("Must pass valid `method` name")
self.inventory_geocollections = self.get_inventory_geocollections()
self.ia_geocollections = self.get_ia_geocollections()
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def load_lcia_data(self):
self.create_inventory_mapping_matrix()
self.create_regionalized_characterization_matrix()
self.create_geo_transform_matrix()
self.normalization_matrix = self.build_normalization_matrix()
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def after_matrix_iteration(self):
self.normalization_matrix = self.build_normalization_matrix()
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def build_normalization_matrix(self):
r"""Get normalization matrix, a diagonal matrix.
.. math::
\textbf{N}_{i,i} = \left[ \sum_{j} \left( \textbf{G} \right)_{i,j} \right]^{-1}
"""
vector = np.array(self.geo_transform_matrix.sum(axis=1)).T
mask = vector > 0
vector[mask] = 1 / vector[mask]
return diags(vector, [0], format="csr", dtype=np.float32)
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def lcia_calculation(self):
"""Do regionalized LCA calculation.
Creates ``self.characterized_inventory``.
"""
self.characterized_inventory = (
self.inv_mapping_matrix
* self.normalization_matrix
* self.geo_transform_matrix
* self.reg_cf_matrix
).T.multiply(self.inventory)
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def results_ia_spatial_scale(self):
if not hasattr(self, "characterized_inventory"):
raise ValueError("Must do lcia calculation first")
return self.reg_cf_matrix.T.multiply(
self.inventory
* self.inv_mapping_matrix
* self.normalization_matrix
* self.geo_transform_matrix
)
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def results_inv_spatial_scale(self):
if not hasattr(self, "characterized_inventory"):
raise ValueError("Must do lcia calculation first")
return (
self.normalization_matrix * self.geo_transform_matrix * self.reg_cf_matrix
).T.multiply(self.inventory * self.inv_mapping_matrix)