Traversal

src.brightwebapp.traversal

_add_branch_information_to_edges_dataframe(df)

Given a dataframe of graph edges with columns consumer_unique_id and producer_unique_id adds a column branch containing a list of

For example, for the following graph:

graph TD
0 --> 1
0 --> 2
0 --> 3
2 --> 4
3 --> 5
5 --> 6

which can be represented as a DataFrame of edges:

consumer_unique_id	producer_unique_id	Comment
0	1	# 1 is terminal producer node of this branch
0	2
0	3
2	4	# 4 is terminal producer node of this branch
3	5
5	6	# 6 is terminal producer node of this branch

the function returns a DataFrame of edges with an additional column branch:

consumer_unique_id	producer_unique_id	branch
0	1	[0, 1]
0	2	[0, 2]
0	3	[0, 3]
2	4	[0, 2, 4]
3	5	[0, 3, 5]
5	6	[0, 3, 5, 6]

See Also

[brightwebapp.traversal._trace_branch_from_last_node][]

Parameters:

Name	Type	Description	Default
df	DataFrame	A dataframe of graph edges. Must contain integer-type columns consumer_unique_id and producer_unique_id.	required

Returns:

Type	Description
DataFrame	A dataframe of graph nodes with a column branch that contains the branch of nodes that lead to the terminal producer node.

Source code in src/brightwebapp/traversal.py

def _add_branch_information_to_edges_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    """
    Given a dataframe of graph edges with columns `consumer_unique_id` and `producer_unique_id`
    adds a column `branch` containing a list of 

    For example, for the following graph:

    ```mermaid
    graph TD
    0 --> 1
    0 --> 2
    0 --> 3
    2 --> 4
    3 --> 5
    5 --> 6
    ```

    which can be represented as a DataFrame of edges:

    | `consumer_unique_id` | `producer_unique_id` | Comment                                      |
    |----------------------|----------------------|----------------------------------------------|
    | 0                    | 1                    | # 1 is terminal producer node of this branch |
    | 0                    | 2                    |                                              |
    | 0                    | 3                    |                                              |
    | 2                    | 4                    | # 4 is terminal producer node of this branch |
    | 3                    | 5                    |                                              |
    | 5                    | 6                    | # 6 is terminal producer node of this branch |

    the function returns a DataFrame of edges with an additional column `branch`:

    | `consumer_unique_id` | `producer_unique_id` | `branch`       |
    |----------------------|----------------------|----------------|
    | 0                    | 1                    | `[0, 1]`       |
    | 0                    | 2                    | `[0, 2]`       |
    | 0                    | 3                    | `[0, 3]`       |
    | 2                    | 4                    | `[0, 2, 4]`    |
    | 3                    | 5                    | `[0, 3, 5]`    |
    | 5                    | 6                    | `[0, 3, 5, 6]` |

    See Also
    --------
    [`brightwebapp.traversal._trace_branch_from_last_node`][]

    Parameters
    ----------
    df : pd.DataFrame
        A dataframe of graph edges.  
        Must contain integer-type columns `consumer_unique_id` and `producer_unique_id`.

    Returns
    -------
    pd.DataFrame
        A dataframe of graph nodes with a column `branch` that contains the branch of nodes that lead to the terminal producer node.
    """
    if df.empty:
        return pd.DataFrame()

    branches: list = []

    for _, row in df.iterrows():
        branch: list = _trace_branch_from_last_node(df, int(row['producer_unique_id']))
        branches.append({
            'producer_unique_id': row['producer_unique_id'],
            'Branch': branch
        })

    return pd.DataFrame(branches)

_edges_dict_to_dataframe(edges)

Returns a Pandas DataFrame with the edges of the graph traversal. Every node in the graph traversal is represented by a row in the DataFrame.

See Also

bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal
[brightwebapp.traversal._traverse_graph][]
[brightwebapp.traversal._nodes_dict_to_dataframe][]

Parameters:

Name	Type	Description	Default
edges	list	A list of edges in the graph traversal. Of the form: [ Edge( consumer_index=-1, consumer_unique_id=-1, producer_index=234, producer_unique_id=0, product_index=153, amount=100.0 ), Edge( consumer_index=234, consumer_unique_id=0, producer_index=78, producer_unique_id=1, product_index=42, amount=1.2520303865276814 ), (...) ]	required

Returns:

Type	Description
DataFrame	A dataframe with the edges of the graph traversal. Of the form: consumer_unique_id producer_unique_id -1 0 0 1 (...) (...)

Raises:

Type	Description
TypeError	If edges is not a list.

Source code in src/brightwebapp/traversal.py

def _edges_dict_to_dataframe(edges: list) -> pd.DataFrame:
    """
    Returns a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
    with the edges of the graph traversal.
    Every node in the graph traversal is represented by a row in the DataFrame.

    See Also
    --------
    [`bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal`](https://docs.brightway.dev/projects/graphtools/en/latest/content/api/bw_graph_tools/graph_traversal/new_node_each_visit/index.html#bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal)  
    [`brightwebapp.traversal._traverse_graph`][]  
    [`brightwebapp.traversal._nodes_dict_to_dataframe`][]

    Parameters
    ----------
    edges : list
        A list of edges in the graph traversal.  
        Of the form:  
        ```python
        [
            Edge(
                consumer_index=-1,
                consumer_unique_id=-1,
                producer_index=234,
                producer_unique_id=0,
                product_index=153,
                amount=100.0
            ),
            Edge(
                consumer_index=234,
                consumer_unique_id=0,
                producer_index=78,
                producer_unique_id=1,
                product_index=42,
                amount=1.2520303865276814
            ),
            (...)
        ]
        ```

    Returns
    -------
    pd.DataFrame
        A dataframe with the edges of the graph traversal.  
        Of the form:  

        | `consumer_unique_id` | `producer_unique_id` |
        |----------------------|----------------------|
        | -1                   | 0                    |
        | 0                    | 1                    |
        | (...)                | (...)                |

    Raises
    ------
    TypeError
        If `edges` is not a list.
    """
    if not isinstance(edges, list):
        raise TypeError(
            f"Expected 'edges' to be a list, but got {type(edges)}."
        )
    if len(edges) < 2:
        return pd.DataFrame()
    else:
        list_of_row_dicts = []
        for current_edge in edges:
            list_of_row_dicts.append(
                {
                    'consumer_unique_id': current_edge.consumer_unique_id,
                    'producer_unique_id': current_edge.producer_unique_id
                }
            )
        return pd.DataFrame(list_of_row_dicts).drop(0)

_nodes_dict_to_dataframe(nodes)

Returns a Pandas DataFrame with human-readable descriptions and emissions values of the nodes in the graph traversal. Every node in the graph traversal is represented by a row in the DataFrame.

Warnings

By default, only the node producing the functional unit is scope 1. All other nodes are scope 3.

See Also

bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal
[brightwebapp.traversal._traverse_graph][]
[brightwebapp.traversal._edges_dict_to_dataframe][]

Parameters:

Name	Type	Description	Default
nodes	dict	A dictionary of nodes in the graph traversal. Of the form: { -1: Node( unique_id=-1, activity_datapackage_id=-1, activity_index=-1, reference_product_datapackage_id=-1, reference_product_index=-1, reference_product_production_amount=1.0, depth=0, supply_amount=1.0, cumulative_score=37.68834121437976, direct_emissions_score=0.0, max_depth=None, direct_emissions_score_outside_specific_flows=0.0, remaining_cumulative_score_outside_specific_flows=0.0, terminal=False ), 0: Node( unique_id=0, activity_datapackage_id=235, activity_index=234, reference_product_datapackage_id=542, reference_product_index=153, reference_product_production_amount=0.9988390803337097, depth=1, supply_amount=100.11622689671918, cumulative_score=37.68834121437975, direct_emissions_score=1.5464815391143154, max_depth=None, direct_emissions_score_outside_specific_flows=0.7561131308224808, remaining_cumulative_score_outside_specific_flows=36.897972806087914, terminal=False ), (...) }	required

Returns:

Type	Description
DataFrame	A dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal. Of the form: UID Scope Name SupplyAmount (...) (...) (...) (...) (...) (...)

Raises:

Type	Description
TypeError	If nodes is not a dictionary.

Source code in src/brightwebapp/traversal.py

def _nodes_dict_to_dataframe(
    nodes: dict,
) -> pd.DataFrame:
    """
    Returns a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
    with human-readable descriptions and emissions values of the nodes in the graph traversal.
    Every node in the graph traversal is represented by a row in the DataFrame.

    Warnings
    --------
    By default, only the node producing the functional unit is scope 1.
    All other nodes are scope 3.

    See Also
    --------
    [`bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal`](https://docs.brightway.dev/projects/graphtools/en/latest/content/api/bw_graph)  
    [`brightwebapp.traversal._traverse_graph`][]  
    [`brightwebapp.traversal._edges_dict_to_dataframe`][]

    Parameters
    ----------
    nodes : dict
        A dictionary of nodes in the graph traversal.  
        Of the form:  
        ```python
        {
        -1: Node(
            unique_id=-1,
            activity_datapackage_id=-1,
            activity_index=-1,
            reference_product_datapackage_id=-1,
            reference_product_index=-1,
            reference_product_production_amount=1.0,
            depth=0,
            supply_amount=1.0,
            cumulative_score=37.68834121437976,
            direct_emissions_score=0.0,
            max_depth=None,
            direct_emissions_score_outside_specific_flows=0.0,
            remaining_cumulative_score_outside_specific_flows=0.0,
            terminal=False
        ),
        0: Node(
            unique_id=0,
            activity_datapackage_id=235,
            activity_index=234,
            reference_product_datapackage_id=542,
            reference_product_index=153,
            reference_product_production_amount=0.9988390803337097,
            depth=1,
            supply_amount=100.11622689671918,
            cumulative_score=37.68834121437975,
            direct_emissions_score=1.5464815391143154,
            max_depth=None,
            direct_emissions_score_outside_specific_flows=0.7561131308224808,
            remaining_cumulative_score_outside_specific_flows=36.897972806087914,
            terminal=False
        ),
        (...)
        }
        ```

    Returns
    -------
    pd.DataFrame
        A dataframe with human-readable descriptions and emissions values of the nodes in the graph traversal.  
        Of the form:  

        | `UID` | `Scope` | `Name` | `SupplyAmount` | (...) |
        |-------|---------|--------|----------------|-------|
        | (...) | (...)   | (...)  | (...)          | (...) |

    Raises
    ------
    TypeError
        If `nodes` is not a dictionary.
    """
    if not isinstance(nodes, dict):
        raise TypeError(
            f"Expected 'nodes' to be a dict, but got {type(nodes)}."
        )

    list_of_row_dicts = []

    for node in nodes.values():
        scope = 3
        if node.unique_id == -1:
            continue
        elif node.unique_id == 0:
            scope = 1
        else:
            pass
        list_of_row_dicts.append(
            {
                'UID': node.unique_id,
                'Scope': scope,
                'Name': bd.get_node(id=node.activity_datapackage_id)['name'],
                'SupplyAmount': node.supply_amount,
                'BurdenIntensity': node.direct_emissions_score/node.supply_amount,
                'Burden(Cumulative)': node.cumulative_score,
                'Burden(Direct)': node.direct_emissions_score + node.direct_emissions_score_outside_specific_flows,
                'Depth': node.depth,
                'activity_datapackage_id': node.activity_datapackage_id,
            }
        )
    return pd.DataFrame(list_of_row_dicts)

_trace_branch_from_last_node(df, unique_id_last_node)

Given a dataframe of graph edges with columns consumer_unique_id and producer_unique_id and the producer_unique_id of the "final node" in a branch, returns the branch of nodes that lead to the final node.

For example, for the following graph:

graph TD
0 --> 1
0 --> 2
0 --> 3
2 --> 4
3 --> 5
5 --> 6

which can be represented as a DataFrame of edges:

consumer_unique_id	producer_unique_id	Comment
0	1	# 1 is terminal producer node of this branch
0	2
0	3
2	4	# 4 is terminal producer node of this branch
3	5
5	6	# 6 is terminal producer node of this branch

For unique_id_last_node = 6, the function returns [0, 3, 5, 6].

See Also

[brightwebapp.traversal._add_branch_information_to_edges_dataframe][]

Example

>>> data = {
>>>     'consumer_unique_id': [0, 0, 0, 2, 3, 5],
>>>     'producer_unique_id': [1, 2, 3, 4, 5, 6],
>>> }
>>> df = pd.DataFrame(data)
>>> trace_branch_from_last_node(df, 6)
[0, 3, 5, 6]

Parameters:

Name	Type	Description	Default
df	DataFrame	Dataframe of graph edges. Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.	required
unique_id_last_node	int	The producer_unique_id integer indicating the last node of a branch to trace.	required

Returns:

Type	Description
list	A list of integers indicating the branch of nodes that lead to the starting node.

Source code in src/brightwebapp/traversal.py

def _trace_branch_from_last_node(
    df: pd.DataFrame,
    unique_id_last_node: int
) -> list:
    """
    Given a dataframe of graph edges with columns `consumer_unique_id` and `producer_unique_id`
    and the `producer_unique_id` of the "final node" in a branch,
    returns the branch of nodes that lead to the final node.

    For example, for the following graph:

    ```mermaid
    graph TD
    0 --> 1
    0 --> 2
    0 --> 3
    2 --> 4
    3 --> 5
    5 --> 6
    ```

    which can be represented as a DataFrame of edges:

    | `consumer_unique_id` | `producer_unique_id` | Comment                                      |
    |----------------------|----------------------|----------------------------------------------|
    | 0                    | 1                    | # 1 is terminal producer node of this branch |
    | 0                    | 2                    |                                              |
    | 0                    | 3                    |                                              |
    | 2                    | 4                    | # 4 is terminal producer node of this branch |
    | 3                    | 5                    |                                              |
    | 5                    | 6                    | # 6 is terminal producer node of this branch |

    For `unique_id_last_node = 6`, the function returns `[0, 3, 5, 6]`.

    See Also
    --------
    [`brightwebapp.traversal._add_branch_information_to_edges_dataframe`][]

    Example
    -------
    ```python
    >>> data = {
    >>>     'consumer_unique_id': [0, 0, 0, 2, 3, 5],
    >>>     'producer_unique_id': [1, 2, 3, 4, 5, 6],
    >>> }
    >>> df = pd.DataFrame(data)
    >>> trace_branch_from_last_node(df, 6)
    [0, 3, 5, 6]
    ```

    Parameters
    ----------
    df : pd.DataFrame
        Dataframe of graph edges. Must contain integer-type columns 'consumer_unique_id' and 'producer_unique_id'.
    unique_id_last_node : int
        The `producer_unique_id` integer indicating the last node of a branch to trace.

    Returns
    -------
    list
        A list of integers indicating the branch of nodes that lead to the starting node.
    """

    branch: list = [unique_id_last_node]

    if unique_id_last_node not in df['producer_unique_id'].values:
        raise ValueError(
            f"unique_id_last_node {unique_id_last_node} not found in 'producer_unique_id' column of the dataframe."
        )

    while True:
        previous_node: int = df[df['producer_unique_id'] == unique_id_last_node]['consumer_unique_id']
        if previous_node.empty:
            break
        unique_id_last_node: int = previous_node.values[0]
        branch.insert(0, int(unique_id_last_node))

    return branch

_traverse_graph(lca, cutoff, biosphere_cutoff, max_calc)

Conducts a graph traversal of a life-cycle assessment calculation using the bw_graph_tools library.

Warnings

This function uses the new (v0.5) API of bw_graph_tools to perform a graph traversal:
NewNodeEachVisitGraphTraversal(lca, settings) and traverse() instead of NewNodeEachVisitGraphTraversal(lca, cutoff) and .calculate().

See Also

[brightwebapp.traversal.perform_lca][]
bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal

Parameters:

Name	Type	Description	Default
lca	LCA	An instance of the bw2calc.LCA class representing the life-cycle assessment calculation.	required
cutoff	float	A float representing the cutoff threshold for the graph traversal.	required
biosphere_cutoff	float	A float representing the biosphere cutoff threshold for the graph traversal.	required
max_calc	int	An integer representing the maximum number of calculations to be performed during the graph traversal.	required

Returns:

Type	Description
dict	A dictionary containing the nodes and edges of the graph traversal. Of the form: { 'nodes': dict, # Dictionary of Node objects 'edges': list # List of Edge objects }

Source code in src/brightwebapp/traversal.py

def _traverse_graph(
    lca: bc.LCA,
    cutoff: float,
    biosphere_cutoff: float,
    max_calc: int,
) -> dict:
    """
    Conducts a graph traversal of a life-cycle assessment calculation
    using the `bw_graph_tools` library.

    Warnings
    --------
    This function uses the new (v0.5) API of `bw_graph_tools` to perform a graph traversal:  
    `NewNodeEachVisitGraphTraversal(lca, settings)` and `traverse()` instead of `NewNodeEachVisitGraphTraversal(lca, cutoff)` and `.calculate()`.

    See Also
    --------
    [`brightwebapp.traversal.perform_lca`][]  
    [`bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal`](https://docs.brightway.dev/projects/graphtools/en/latest/content/api/bw_graph_tools/graph_traversal/new_node_each_visit/index.html#bw_graph_tools.graph_traversal.new_node_each_visit.NewNodeEachVisitGraphTraversal)

    Parameters
    ----------
    lca : bc.LCA
        An instance of the `bw2calc.LCA` class representing the life-cycle assessment calculation.
    cutoff : float
        A float representing the cutoff threshold for the graph traversal.
    biosphere_cutoff : float
        A float representing the biosphere cutoff threshold for the graph traversal.
    max_calc : int
        An integer representing the maximum number of calculations to be performed during the graph traversal.

    Returns
    -------
    dict
        A dictionary containing the nodes and edges of the graph traversal.  
        Of the form:  
        ```python
        {
            'nodes': dict,  # Dictionary of Node objects
            'edges': list   # List of Edge objects
        }
        ```
    """
    traversal = bgt.NewNodeEachVisitGraphTraversal(
        lca=lca,
        settings=bgt.GraphTraversalSettings(
            cutoff=cutoff,
            biosphere_cutoff=biosphere_cutoff,
            max_calc=max_calc,
        )
    )
    traversal.traverse()
    return {
        'nodes': traversal.nodes,
        'edges': traversal.edges,
    }

perform_graph_traversal(cutoff, biosphere_cutoff, max_calc, return_format, lca=None, method=None, demand=None)

Performs a graph traversal of a life-cycle assessment calculation and returns a DataFrame with the nodes and edges of the graph traversal.

Notes

Accepts either an lca object returned by the [brightwebapp.traversal.perform_lca][] function or the method and demand variables.

See Also

[brightwebapp.traversal.perform_lca][]
bw_graph_tools

Parameters:

Name	Type	Description	Default
cutoff	float	A float representing the cutoff threshold for the graph traversal. This is used to limit the depth of the traversal and the amount of data processed.	required
biosphere_cutoff	float	A float representing the biosphere cutoff threshold for the graph traversal. This is used to limit the amount of biosphere data processed.	required
max_calc	int	An integer representing the maximum number of calculations to be performed during the graph traversal. This is used to limit the amount of data processed and the depth of the traversal.	required
return_format	str	A string indicating the format of the return value. Can be either 'dataframe' or 'csv'.	required
lca	LCA | None	An instance of the bw2calc.LCA class representing the life-cycle assessment calculation	None
method	tuple	A tuple representing the method to be used for the life-cycle assessment. For example: ('Impact Potential', 'GCC')	None
demand	dict	A dictionary representing the reference product demand for the life-cycle assessment calculation. The key is a bw2data node (bw2data.backends.proxies.Activity) and the values is amounts of the activities to be produced. For example: {bd.get_node(code='bike'): 1}	None

Returns:

Type	Description
DataFrame	If return_format is 'dataframe': A DataFrame with the nodes and edges of the graph traversal. The DataFrame contains the following columns: UID: Unique identifier of the node Scope: Scope of the node (1 for functional unit, 3 for other nodes) Name: Name of the node SupplyAmount: Supply amount of the node BurdenIntensity: Burden intensity of the node Burden(Direct): Direct burden of the node Depth: Depth of the node in the graph Branch: A list of unique identifiers of the nodes in the branch leading to the terminal producer node
str	If return_format is 'csv': A CSV string representation of the DataFrame with the nodes and edges of the graph traversal. Separated by , without an index column: 'UID,Scope,Name,SupplyAmount,BurdenIntensity,Burden(Direct),Depth,Branch 0,1,Automobiles; at manufacturer,1.0011622689671917,0.01544686198282003,0.023025946699367965,1, 1,3,Vehicle electrical and electronic equipment; at manufacturer,0.013377550027867835,0.0093078791680739,0.0002490332384485149,2,"[0, 1]" 2,3,Transmission and power train parts; at manufacturer,0.09230018343308832,0.016065891653621933,0.002965769493290854,2,"[0, 2]" (...)

Raises:

Type	Description
ValueError	If return_format is not 'dataframe' or 'csv'. If no edges are found in the graph traversal.

Source code in src/brightwebapp/traversal.py

def perform_graph_traversal(
    cutoff: float,
    biosphere_cutoff: float,
    max_calc: int,
    return_format: str,
    lca: bc.LCA = None,
    method: tuple = None,
    demand: dict = None,
) -> pd.DataFrame | str:
    """
    Performs a graph traversal of a life-cycle assessment calculation
    and returns a DataFrame with the nodes and edges of the graph traversal.

    Notes
    -----
    Accepts either an `lca` object returned by the [`brightwebapp.traversal.perform_lca`][] function
    or the `method` and `demand` variables.

    See Also
    --------
    [`brightwebapp.traversal.perform_lca`][]  
    [`bw_graph_tools`](https://docs.brightway.dev/projects/graphtools/en/latest/content/api/bw_graph_tools/index.html)

    Parameters
    ----------
    cutoff : float
        A float representing the cutoff threshold for the graph traversal.
        This is used to limit the depth of the traversal and the amount of data processed.
    biosphere_cutoff : float
        A float representing the biosphere cutoff threshold for the graph traversal.
        This is used to limit the amount of biosphere data processed.
    max_calc : int
        An integer representing the maximum number of calculations to be performed during the graph traversal.
        This is used to limit the amount of data processed and the depth of the traversal.
    return_format : str
        A string indicating the format of the return value.
        Can be either `'dataframe'` or `'csv'`.
    lca : bc.LCA | None, optional
        An instance of the `bw2calc.LCA` class representing the life-cycle assessment calculation
    method : tuple 
        A tuple representing the method to be used for the life-cycle assessment.

        For example:  

        ```python
        ('Impact Potential', 'GCC')
        ```
    demand : dict
        A dictionary representing the reference product demand for the life-cycle assessment calculation.  

        The key is a `bw2data` node ([`bw2data.backends.proxies.Activity`](https://docs.brightway.dev/en/latest/content/api/bw2data/backends/proxies/index.html#bw2data.backends.proxies.Activity)) 
        and the values is amounts of the activities to be produced.

        For example:  

        ```python
        {bd.get_node(code='bike'): 1}
        ``` 

    Returns
    -------
    pd.DataFrame
        **If `return_format` is `'dataframe'`**:  

        A DataFrame with the nodes and edges of the graph traversal.  
        The DataFrame contains the following columns:  

        - `UID`: Unique identifier of the node
        - `Scope`: Scope of the node (1 for functional unit, 3 for other nodes)
        - `Name`: Name of the node
        - `SupplyAmount`: Supply amount of the node
        - `BurdenIntensity`: Burden intensity of the node
        - `Burden(Direct)`: Direct burden of the node
        - `Depth`: Depth of the node in the graph
        - `Branch`: A list of unique identifiers of the nodes in the branch leading to the terminal producer node 
    str
        **If `return_format` is `'csv'`**:  

        A CSV string representation of the DataFrame with the nodes and edges of the graph traversal.  
        Separated by `,` without an index column:

        ```csv
        'UID,Scope,Name,SupplyAmount,BurdenIntensity,Burden(Direct),Depth,Branch\n
        0,1,Automobiles; at manufacturer,1.0011622689671917,0.01544686198282003,0.023025946699367965,1,\n
        1,3,Vehicle electrical and electronic equipment; at manufacturer,0.013377550027867835,0.0093078791680739,0.0002490332384485149,2,"[0, 1]"\n
        2,3,Transmission and power train parts; at manufacturer,0.09230018343308832,0.016065891653621933,0.002965769493290854,2,"[0, 2]"\n
        (...)
        ```

    Raises
    ------
    ValueError
        If `return_format` is not `'dataframe'` or `'csv'`.  
        If no edges are found in the graph traversal.
    """
    if return_format not in ['dataframe', 'csv']:
        raise ValueError(
            f"Invalid return_format '{return_format}'. "
            "Expected 'dataframe' or 'csv'."
        )
    if lca is None:
        if method is None or demand is None:
            raise ValueError(
                "If 'lca' is not provided, both 'method' and 'demand' must be provided."
            )
        lca = perform_lca(
            demand=demand,
            method=method
        )
    if lca is not None and (method is not None or demand is not None):
        print(
            "Warning: Both 'lca' and 'method'/'demand' are provided. "
            "'lca' will be used and 'method'/'demand' will be ignored."
        )
    if len(demand) != 1:
        raise ValueError(
            "Demand dictionary must contain exactly one activity."
        )
    if not isinstance(next(iter(demand)), Activity):
        raise ValueError(
            "The key in the demand dictionary must be a valid bw2data node dictionary."
        )

    traversal: dict = _traverse_graph(
        lca=lca,
        cutoff=cutoff,
        biosphere_cutoff=biosphere_cutoff,
        max_calc=max_calc,
    )
    df_graph_traversal_nodes: pd.DataFrame = _nodes_dict_to_dataframe(traversal['nodes'])
    df_graph_traversal_edges: pd.DataFrame = _edges_dict_to_dataframe(traversal['edges'])
    if df_graph_traversal_edges.empty:
        raise ValueError(
            "No edges found in the graph traversal. "
            "This may be due to a cutoff value that is too high, "
            "or a demand that does not lead to any edges."
        )
    else:
        df_graph_traversal_edges = _add_branch_information_to_edges_dataframe(df_graph_traversal_edges)
        df_traversal = pd.merge(
            df_graph_traversal_nodes,
            df_graph_traversal_edges,
            left_on='UID',
            right_on='producer_unique_id',
            how='left'
        )
    df_traversal = df_traversal.drop(columns=['producer_unique_id', 'activity_datapackage_id'])
    if return_format == 'dataframe':
        return df_traversal
    elif return_format == 'csv':
        return df_traversal.to_csv(index=False)

perform_lca(demand, method)

Performs a life-cycle assessment calculation using the bw2calc library.

See Also

bw2calc.LCA

Parameters:

Name	Type	Description	Default
demand	dict	A dictionary representing the reference product demand for the life-cycle assessment calculation. The key is a bw2data node (bw2data.backends.proxies.Activity) and the values is amounts of the activities to be produced. For example: {bd.get_node(code='bike'): 1}	required
method	tuple	A tuple representing the method to be used for the life-cycle assessment. For example: ('Impact Potential', 'GCC')	required

Warnings

The demand dictionary must contain exactly one activity.

See Also

Brightway Documentation: LCA Calculations

Returns:

Type	Description
LCA	An instance of the bw2calc.LCA class representing the life-cycle assessment calculation.

Raises:

Type	Description
ValueError	If demand does not contain exactly one activity. If the key in demand is not a valid bw2data node dictionary.

Source code in src/brightwebapp/traversal.py

def perform_lca(demand: dict, method: tuple) -> bc.LCA:
    """
    Performs a life-cycle assessment calculation using the `bw2calc` library.

    See Also
    --------
    [`bw2calc.LCA`](https://2.docs.brightway.dev/projects/bw2calc/en/latest/api/bw2calc/lca.html#bw2calc.lca.LCA)

    Parameters
    ----------
    demand : dict
        A dictionary representing the reference product demand for the life-cycle assessment calculation.  

        The key is a `bw2data` node ([`bw2data.backends.proxies.Activity`](https://docs.brightway.dev/en/latest/content/api/bw2data/backends/proxies/index.html#bw2data.backends.proxies.Activity)) 
        and the values is amounts of the activities to be produced.

        For example:  

        ```python
        {bd.get_node(code='bike'): 1}
        ``` 
    method : tuple 
        A tuple representing the method to be used for the life-cycle assessment.

        For example:  

        ```python
        ('Impact Potential', 'GCC')
        ```

    Warnings
    --------
    The `demand` dictionary must contain exactly one activity.

    See Also
    --------
    [Brightway Documentation: LCA Calculations](https://docs.brightway.dev/en/latest/content/cheatsheet/lca.html)

    Returns
    -------
    bc.LCA
        An instance of the `bw2calc.LCA` class representing the life-cycle assessment calculation.

    Raises
    ------
    ValueError
        If `demand` does not contain exactly one activity.
        If the key in `demand` is not a valid `bw2data` node dictionary.
    """
    if len(demand) != 1:
        raise ValueError(
            "Demand dictionary must contain exactly one activity."
        )
    if not isinstance(next(iter(demand)), Activity):
        raise ValueError(
            "The key in the demand dictionary must be a valid bw2data node dictionary."
        )

    my_functional_unit, data_objs, _ = bd.prepare_lca_inputs(
        demand=demand,
        method=method
    )
    lca = bc.LCA(
        demand=my_functional_unit,
        data_objs=data_objs,
    )
    lca.lci()
    lca.lcia()
    return lca