bw2calc.indexing

Module Contents

Functions

index_with_arrays(array_from, array_to, mapping)	Map array_from keys to array_to values using the dictionary mapping.
index_with_searchsorted(array_from, array_to)	Build a dictionary from the sorted, unique elements of an array, and map this dictionary from array_from to array_to.

bw2calc.indexing.index_with_arrays(array_from, array_to, mapping)

Map array_from keys to array_to values using the dictionary mapping.

Turns the keys and values of mapping into index arrays.

This is needed to take the flow, input, and output columns, which can be arbitrarily large integers, and transform them to matrix indices, which start from zero.

Here is an example:

import numpy as np
a_f = np.array((1, 2, 3, 4))
a_t = np.zeros(4)
mapping = {1: 5, 2: 6, 3: 7, 4: 8}
index_with_arrays(a_f, a_t, mapping)
# => a_t is now [5, 6, 7, 8]

Parameters

• array_from (*) – 1-dimensional integer numpy array.

• array_to (*) – 1-dimensional integer numpy array.

• mapping (*) – Dictionary that links mapping indices to row or col indices, e.g. {34: 3}.

Operates in place. Doesn’t return anything.

bw2calc.indexing.index_with_searchsorted(array_from, array_to)

Build a dictionary from the sorted, unique elements of an array, and map this dictionary from array_from to array_to.

Adapted from http://stackoverflow.com/questions/3403973/fast-replacement-of-values-in-a-numpy-array.

Here is an example:

import numpy as np
array = np.array((4, 8, 6, 2, 4))
output = np.zeros(5)
index_with_searchsorted(array, output)
# => returns {2: 0, 4: 1, 6: 2, 8: 3}
# and `output` is [1, 3, 2, 0, 1]

array_from and array_to are arrays of integers.

Returns a dictionary that maps the sorted, unique elements of array_from to integers starting with zero.