Source code for bw2data.weighting_normalization

# -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *

from .ia_data_store import ImpactAssessmentDataStore
from .meta import weightings, mapping, normalizations
from .project import writable_project
from .utils import MAX_INT_32, numpy_string
from .validate import weighting_validator, normalization_validator
import numpy as np

[docs]class Weighting(ImpactAssessmentDataStore): """ LCIA weighting data - used to combine or compare different impact categories. The data schema for weighting is a one-element list: .. code-block:: python Schema(All( [uncertainty_dict], Length(min=1, max=1) )) """
[docs] _metadata = weightings
[docs] validator = weighting_validator
[docs] dtype_fields = []
[docs] def write(self, data): """Because of DataStore assumptions, need a one-element list""" if not isinstance(data, list) or not len(data) == 1: raise ValueError("Weighting data must be one-element list") super(Weighting, self).write(data)
[docs] def process_data(self, row): """Return an empty tuple (as ``dtype_fields`` is empty), and the weighting uncertainty dictionary.""" return ( (), # don't know much, row # but I know I love you )
[docs]class Normalization(ImpactAssessmentDataStore): """ LCIA normalization data - used to transform meaningful units, like mass or damage, into "person-equivalents" or some such thing. The data schema for IA normalization is: .. code-block:: python Schema([ [valid_tuple, maybe_uncertainty] ]) where: * ``valid_tuple`` is a dataset identifier, like ``("biosphere", "CO2")`` * ``maybe_uncertainty`` is either a number or an uncertainty dictionary """
[docs] _metadata = normalizations
[docs] validator = normalization_validator
[docs] dtype_fields = [ (numpy_string('flow'), np.uint32), (numpy_string('index'), np.uint32), ]
[docs] def add_mappings(self, data): """Add each normalization flow (should be biosphere flows) to global mapping""" mapping.add({obj[0] for obj in data})
[docs] def process_data(self, row): """Return values that match ``dtype_fields``, as well as number or uncertainty dictionary""" return ( mapping[row[0]], MAX_INT_32, ), row[1]