# Brightway2-analyzer¶

## Contribution analysis¶

class bw2analyzer.contribution.ContributionAnalysis
annotate(sorted_data, rev_mapping)

Reverse the mapping from database ids to array indices

annotated_top_emissions(lca, names=True, **kwargs)

Get list of most damaging biosphere flows in an LCA, sorted by abs(direct impact).

Returns a list of tuples: (lca score, inventory amount, activity). If names is False, they returns the process key as the last element.

annotated_top_processes(lca, names=True, **kwargs)

Get list of most damaging processes in an LCA, sorted by abs(direct impact).

Returns a list of tuples: (lca score, supply, activity). If names is False, they returns the process key as the last element.

d3_treemap(matrix, rev_bio, rev_techno, limit=0.025, limit_type='percent')

Construct treemap input data structure for LCA result. Output like:

{
"name": "LCA result",
"children": [{
"name": process 1,
"children": [
{"name": emission 1, "size": score},
{"name": emission 2, "size": score},
],
}]
}

sort_array(data, limit=25, limit_type='number', total=None)

Common sorting function for all top methods. Sorts by highest value first.

Operates in either number or percent mode. In number mode, return limit values. In percent mode, return all values >= (total * limit); where 0 < limit <= 1.

Returns 2-d numpy array of sorted values and row indices, e.g.:

ContributionAnalysis().sort_array((1., 3., 2.))


returns

(
(3, 1),
(2, 2),
(1, 0)
)

Args:
• data (numpy array): A 1-d array of values to sort.

• limit (number, default=25): Number of values to return, or percentage cutoff.

• limit_type (str, default=number): Either number or percent.

• total (number, default=None): Optional specification of summed data total.

Returns:

2-d numpy array of values and row indices.

top_emissions(matrix, **kwargs)

Return an array of [value, index] biosphere emissions.

top_matrix(matrix, rows=5, cols=5)

Find most important (i.e. highest summed) rows and columns in a matrix, as well as the most corresponding non-zero individual elements in the top rows and columns.

Only returns matrix values which are in the top rows and columns. Element values are returned as a tuple: (row, col, row index in top rows, col index in top cols, value).

Example:

matrix = [
[0, 0, 1, 0],
[2, 0, 4, 0],
[3, 0, 1, 1],
[0, 7, 0, 1],
]


In this matrix, the row sums are (1, 6, 5, 8), and the columns sums are (5, 7, 6, 2). Therefore, the top rows are (3, 1) and the top columns are (1, 2). The result would therefore be:

(
(
(3, 1, 0, 0, 7),
(3, 2, 0, 1, 1),
(1, 2, 1, 1, 4)
),
(3, 1),
(1, 2)
)

Args:
• matrix (array or matrix): Any Python object that supports the .sum(axis=) syntax.

• rows (int): Number of rows to select.

• cols (int): Number of columns to select.

Returns:

(elements, top rows, top columns)

top_processes(matrix, **kwargs)

Return an array of [value, index] technosphere processes.

## Supply chain traversal¶

bw2analyzer.traverse_tagged_databases(functional_unit, method, label='tag', default_tag='other', secondary_tags=[], fg_databases=None)

Traverse a functional unit throughout its foreground database(s) or the listed databses in fg_databses, and group impacts by tag label.

Contribution analysis work by linking impacts to individual activities. However, you also might want to group impacts in other ways. For example, give individual biosphere exchanges their own grouping, or aggregate two activities together.

Consider this example system, where the letters are the tag labels, and the numbers are exchange amounts. The functional unit is one unit of the tree root.

In this supply chain, tags are applied to activities and biosphere exchanges. If a biosphere exchange is not tagged, it inherits the tag of its producing activity. Similarly, links to other databases are assessed with the usual LCA machinery, and the total LCA score is tagged according to its consuming activity. If an activity does not have a tag, a default tag is applied.

We can change our visualization to show the use of the default tags:

And then we can manually calculate the tagged impacts. Normally we would need to know the actual biosphere flows and their respective characterization factors (CF), but in this example we assume that each CF is one. Our result, group by tags, would therefore be:

• A: $$6 + 27 = 33$$

• B: $$30 + 44 = 74$$

• C: $$5 + 16 + 48 = 69$$

• D: $$14$$

This function will only traverse the foreground database, i.e. the database of the functional unit activity. A functional unit can have multiple starting nodes; in this case, all foreground databases are traversed.

Input arguments:

• functional_unit: A functional unit dictionary, e.g. {("foo", "bar"): 42}.

• method: A method name, e.g. ("foo", "bar")

• label: The label of the tag classifier. Default is "tag"

• default_tag: The tag classifier to use if none was given. Default is "other"

• secondary_tags: List of tuples in the format (secondary_label, secondary_default_tag). Default is empty list.

• fg_databases: a list of foreground databases to be traversed, e.g. [‘foreground’, ‘biomass’, ‘machinery’]

It’s not recommended to include all databases of a project in the list to be traversed, especially not ecoinvent itself

Returns:

Aggregated tags dictionary from aggregate_tagged_graph, and tagged supply chain graph from recurse_tagged_database.

This function uses the following help functions:

bw2analyzer.tagged.aggregate_tagged_graph(graph)

Aggregate a graph produced by recurse_tagged_database by the provided tags.

Outputs a dictionary with keys of tags and numeric values.

{'a tag': summed LCIA scores}

bw2analyzer.tagged.recurse_tagged_database(activity, amount, method_dict, lca, label, default_tag, secondary_tags=[], fg_databases=None, warned=False)

Traverse a foreground database and assess activities and biosphere flows by tags.

Input arguments:

• activity: Activity tuple or object

• amount: float

• method_dict: Dictionary of biosphere flow tuples to CFs, e.g. {("biosphere", "foo"): 3}

• lca: An LCA object that is already initialized, i.e. has already calculated LCI and LCIA with same method as in method_dict

• label: string

• default_tag: string

• secondary_tags: List of tuples in the format (secondary_label, secondary_default_tag). Default is empty list.

• fg_databases: a list of foreground databases to be traversed, e.g. [‘foreground’, ‘biomass’, ‘machinery’]

It’s not recommended to include all databases of a project in the list to be traversed, especially not ecoinvent itself

Returns:

{
'activity': activity object,
'amount': float,
'tag': string,
'secondary_tags': [list of strings],
'impact': float (impact of inputs from outside foreground database),
'biosphere': [{
'amount': float,
'impact': float,
'tag': string,
'secondary_tags': [list of strings]
}],
'technosphere': [this data structure]
}


## LCA reports¶

class bw2analyzer.report.SerializedLCAReport(activity, method, iterations=10000, cpus=None, outliers=0.025)

A complete LCA report (i.e. LCA score, Monte Carlo uncertainty analysis, contribution analysis) that can be serialized to a defined standard.

calculate()

Calculate LCA report data

get_force_directed(nodes, edges, lca)

Get graph traversal results

get_monte_carlo()

Get Monte Carlo results

upload()

write()

Write report data to file

## PageRank algorithm¶

class bw2analyzer.page_rank.PageRank(database)
page_rank(technosphere, alpha=0.85, max_iter=100, tol=1e-06)

Return the PageRank of the nodes in the graph.

PageRank computes a ranking of the nodes in the graph G based on the structure of the incoming links. It was originally designed as an algorithm to rank web pages.

The eigenvector calculation uses power iteration with a SciPy sparse matrix representation.

Args:
• technosphere (scipy sparse matrix): The technosphere matrix.

• alpha (float, optional): Damping parameter for PageRank, default=0.85

Returns:
• Dictionary of nodes (activity codes) with value as PageRank

References

1

A. Langville and C. Meyer, “A survey of eigenvector methods of web information retrieval.” http://citeseer.ist.psu.edu/713792.html

2

Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry, The PageRank citation ranking: Bringing order to the Web. 1999 http://dbpubs.stanford.edu:8090/pub/showDoc.Fulltext?lang=en&doc=1999-66&format=pdf

## Comparison functions¶

bw2analyzer.comparisons.compare_activities_by_grouped_leaves(activities, lcia_method, mode='relative', max_level=4, cutoff=0.0075, output_format='list', str_length=50)

Compare activities by the impact of their different inputs, aggregated by the product classification of those inputs.

Args:

activities: list of Activity instances. lcia_method: tuple. LCIA method to use when traversing supply chain graph. mode: str. If “relative” (default), results are returned as a fraction of total input. Otherwise, results are absolute impact per input exchange. max_level: int. Maximum level in supply chain to examine. cutoff: float. Fraction of total impact to cutoff supply chain graph traversal at. output_format: str. See below. str_length; int. If output_format is html, this controls how many characters each column label can have.

Raises:

ValueError: activities is malformed.

Returns:

Depends on output_format:

• list: Tuple of (column labels, data)

• html: HTML string that will print nicely in Jupyter notebooks.

• pandas: a pandas DataFrame.

bw2analyzer.utils.print_recursive_calculation(activity, lcia_method, amount=1, max_level=3, cutoff=0.01, file_obj=None, tab_character='  ', level=0, lca_obj=None, total_score=None, first=True)

Traverse a supply chain graph, and calculate the LCA scores of each component. Prints the result with the format:

{tab_character * level }{fraction of total score} ({absolute LCA score for this input} | {amount of input}) {input activity}

Args:

activity: Activity. The starting point of the supply chain graph. lcia_method: tuple. LCIA method to use when traversing supply chain graph. amount: int. Amount of activity to assess. max_level: int. Maximum depth to traverse. cutoff: float. Fraction of total score to use as cutoff when deciding whether to traverse deeper. file_obj: File-like object (supports .write), optional. Output will be written to this object if provided. tab_character: str. Character to use to indicate indentation.

Internal args (used during recursion, do not touch);

level: int. lca_obj: LCA. total_score: float. first: bool.

Returns:

Nothing. Prints to sys.stdout or file_obj

bw2analyzer.utils.print_recursive_supply_chain(activity, amount=1, max_level=2, cutoff=0, file_obj=None, tab_character='  ', level=0)

Traverse a supply chain graph, and prints the inputs of each component.

This function is only for exploration; use bw2calc.GraphTraversal for a better performing function.

The results displayed here can also be incorrect if

Args:

activity: Activity. The starting point of the supply chain graph. amount: int. Supply chain inputs will be scaled to this value. max_level: int. Max depth to search for. cutoff: float. Inputs with amounts less than amount * cutoff will not be printed or traversed further. file_obj: File-like object (supports .write), optional. Output will be written to this object if provided. tab_character: str. Character to use to indicate indentation. level: int. Current level of the calculation. Only used internally, do not touch.

Returns:

Nothing. Prints to stdout or file_obj

bw2analyzer.comparisons.find_differences_in_inputs(activity, rel_tol=0.0001, abs_tol=1e-09, locations=None, as_dataframe=False)

Given an Activity, try to see if other activities in the same database (with the same name and reference product) have the same input levels.

Tolerance values are inputs to math.isclose.

If differences are present, a difference dictionary is constructed, with the form:

{Activity instance: [(name of input flow (str), amount)]}


Note that this doesn’t reference a specific exchange, but rather sums all exchanges with the same input reference product.

Assumes that all similar activities produce the same amount of reference product.

(x, y), where x is the number of similar activities, and y is a dictionary of the differences. This dictionary is empty if no differences are found.

Args:

activity: Activity. Activity to analyze. rel_tol: float. Relative tolerance to decide if two inputs are the same. See above. abs_tol: float. Absolute tolerance to decide if two inputs are the same. See above. locations: list, optional. Locations to restrict comparison to, if present. as_dataframe: bool. Return results as pandas DataFrame.

Returns:

dict or pandas.DataFrame.

bw2analyzer.comparisons.compare_activities_by_lcia_score(activities, lcia_method, band=0.1)

Compare selected activities to see if they are substantially different.

Substantially different means that all LCIA scores lie within a band of band * max_lcia_score.

Inputs:

activities: List of Activity objects. lcia_method: Tuple identifying a Method

Returns:

Nothing, but prints to stdout.