dopo.sector_lca_scores

Generates and saves Life Cycle Assessment (LCA) scores plots for various sectors to an Excel file.

The module computes LCA scores for activities and methods, generates plots (dot plots and stacked bar charts), and saves them along with LCA scores tables to an Excel file. Includes helper functions for processing, plotting, and formatting data.

Functions

_add_sector_marker(df, sector)	Adds a sector marker to the DataFrame for labeling and identification purposes.
_add_statistics(df[, column_name])	Adds statistical indicators to a DataFrame for plotting purposes.
_clean_column_labels(df)	Cleans and formats column labels in the DataFrame by removing unnecessary numbers and colons.
_compare_activities_by_grouped_leaves(activities, ...)	Adapted birghtway2 analyzer function. It stores additional labels and data per activity.
_compare_activities_multiple_methods(activities_list, ...)	Compares a list of activities using multiple LCA methods and stores the results in a dictionary
_find_first_input_column(df)	Identifies the index of the first column in a DataFrame that contains input contribution data.
_sector_lca_scores(activity_dict, method_dict[, cutoff])	Generates LCA score tables for each sector's activity list, including total scores and CPC
_sector_lca_scores_to_excel(scores_dict, excel_file_name)	Writes LCA scores to an Excel file, organizing data by sector and method.
_small_inputs_to_other_column(dataframes_dict[, cutoff])	Aggregates insignificant input contributions into an 'other' column for each DataFrame in the
sector_lca_scores_plots(activity_dict, method_dict, ...)	Generate plots of Life Cycle Assessment (LCA) scores for different sectors and save them to an

Module Contents

dopo.sector_lca_scores._add_sector_marker(df, sector)

Adds a sector marker to the DataFrame for labeling and identification purposes.

This function is used to add a new column to the DataFrame that indicates the sector associated with the data. The sector information is useful for identifying and labeling data in plots and Excel sheets. The column is positioned immediately after the ‘product’ column if it exists, or appended at the end if ‘product’ is not present.

Parameters

dfpandas.DataFrame

The DataFrame to which the sector marker will be added.

sectorstr

The name of the sector to be added as a marker.

Returns

pandas.DataFrame

The DataFrame with an added ‘sector’ column, positioned immediately after the ‘product’ column if present, or at the end otherwise.

dopo.sector_lca_scores._add_statistics(df, column_name='total')

Adds statistical indicators to a DataFrame for plotting purposes.

This function computes several statistics based on the values in the specified column (column_name). It adds columns for ranking, mean, standard deviation bounds, and interquartile range (IQR). The statistics are added to aid in visual analysis and plotting.

Parameters

dfpandas.DataFrame

The DataFrame to which statistical indicators will be added.

column_namestr, optional

The name of the column on which to base the statistics. Default is ‘total’.

Returns

pandas.DataFrame

The updated DataFrame with added columns for ranking, mean, standard deviation bounds, and IQR.

dopo.sector_lca_scores._clean_column_labels(df)

Cleans and formats column labels in the DataFrame by removing unnecessary numbers and colons.

This function is used to standardize column headers by removing leading numbers and colons, which can be present in columns used for input contributions or other data. It should be called after _find_first_input_column to ensure column order and identification are correctly handled.

Parameters

dfpandas.DataFrame

The DataFrame whose column labels are to be cleaned.

Returns

pandas.DataFrame

The DataFrame with formatted column labels, where unnecessary numbers and colons have been removed.

dopo.sector_lca_scores._compare_activities_by_grouped_leaves(activities, lcia_method, mode='relative', max_level=4, cutoff=0.0075, output_format='list', str_length=50)

Adapted birghtway2 analyzer function. It stores additional labels and data per activity.

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.

dopo.sector_lca_scores._compare_activities_multiple_methods(activities_list, methods, identifier, output_format='pandas', mode='absolute')

Compares a list of activities using multiple LCA methods and stores the results in a dictionary of DataFrames.

This function generates comparison results for each method in methods, formats them into DataFrames, and organizes them in a dictionary where the keys are method-specific names derived from the identifier and method details. Each DataFrame contains total scores and input contributions, with columns ordered and indexed appropriately.

Parameters

activities_listlist

A list of activities to be compared.

methodsdict

A dictionary where keys are method names and values are dictionaries with the key “object” being a Brightway Method object used for comparisons.

identifierstr

A string used to construct unique variable names for the comparison results (e.g., sector name).

output_formatstr, optional

The format for the output DataFrame. Default is “pandas”. Other formats can be specified if supported.

modestr, optional

The mode of comparison. Options are “absolute” (default) and “relative”.

Returns

dict

A dictionary where each key is a unique name derived from the identifier and method name, and each value is a DataFrame containing the comparison results.

dopo.sector_lca_scores._find_first_input_column(df)

Identifies the index of the first column in a DataFrame that contains input contribution data.

This function is used to locate the column in the DataFrame that holds input contribution data, which is essential for dynamically selecting the correct column for plotting. It ensures compatibility with DataFrames that may have different column orders or names, such as those including “direct emissions.”

Parameters

dfpandas.DataFrame

The DataFrame in which to find the first input contribution column.

Returns

int or None

The index of the first column containing input data. Returns None if no such column is found.

dopo.sector_lca_scores._sector_lca_scores(activity_dict, method_dict, cutoff=0.01)

Generates LCA score tables for each sector’s activity list, including total scores and CPC input contributions.

This function calculates LCA scores for activities within each sector using methods specified in the method_dict. Inputs below or equal to the cutoff value are summarized in an “other” column.

Parameters

activity_dictdict

A dictionary returned by the process_yaml_files function. It should contain sector names as keys, each with an ‘activities’ entry holding the list of activities for that sector.

method_dictdict

A dictionary created with the MethodFinder class, containing methods for LCA score calculation.

cutofffloat, optional

A threshold value for summarizing inputs below or equal to this value in an “other” column. Default is 0.02.

Returns

dict

The updated dictionary (formerly activity_dict) with an additional key ‘lca_scores’ for each sector. This contains the calculated LCA scores by method.

dopo.sector_lca_scores._sector_lca_scores_to_excel(scores_dict, excel_file_name)

Writes LCA scores to an Excel file, organizing data by sector and method.

For each sector in the scores_dict, this function performs the following: - Creates a DataFrame for each method within that sector. - Shortens column labels by removing CPC codes. - Adds a sector name marker to facilitate tracking in Excel. - Adds statistical columns for plotting purposes. - Creates a dictionary of column index positions used for plotting, making it dynamic and

avoiding hardcoded column indices.

Parameters

scores_dictdict

A dictionary where each key is a sector name and each value contains LCA scores and other relevant data. The structure should be compatible with the output of the sector_lca_scores function.

excel_file_namestr

The name of the Excel file to be created, including the file extension (e.g., ‘lca_scores.xlsx’).

Returns

dict

A dictionary where each key is a “sector_method” string and each value is another dictionary mapping column names to their index positions. This dictionary aids in dynamic plotting.

dopo.sector_lca_scores._small_inputs_to_other_column(dataframes_dict, cutoff=0.01)

Aggregates insignificant input contributions into an ‘other’ column for each DataFrame in the input dictionary.

Contributions that are less than or equal to the specified cutoff value (relative to the ‘total’ column) are combined into a new ‘other’ column. The original columns with these contributions are set to zero. Columns that end up containing only zeros are removed, and columns named None or “Unnamed” are also combined into the ‘other’ column before removal.

Parameters

dataframes_dictdict

A dictionary where each key corresponds to a DataFrame. Each DataFrame should contain a ‘total’ column and may include columns to be aggregated into the ‘other’ column based on their contributions.

cutofffloat, optional

The cutoff value for determining insignificant contributions. Contributions less than or equal to this value (relative to the ‘total’ column) are aggregated into the ‘other’ column. Default is 0.01.

Returns

dict

A dictionary with the same keys as dataframes_dict, but with each DataFrame updated to include an ‘other’ column and without insignificant columns.

dopo.sector_lca_scores.sector_lca_scores_plots(activity_dict, method_dict, excel_file_name, cutoff=0.01)

Generate plots of Life Cycle Assessment (LCA) scores for different sectors and save them to an Excel file.

This function calculates LCA scores for a set of activities and methods, then generates plots (dot plots and stacked bar charts) based on these scores. The generated plots are saved to an Excel file.

Args:

activity_dict (dict): A dictionary where keys are activity names or IDs and values are

corresponding activity data.

method_dict (dict): A dictionary where keys are method names or IDs and values are

corresponding method data.

excel_file_name (str): The name of the Excel file where the LCA scores tables and plots will

be saved.

cutoff (float, optional): A cutoff value for filtering LCA scores. Any scores below this

value will be excluded. Default is 0.01.

Returns:

None

The function performs the following steps: 1. Generates LCA scores tables based on the provided activity and method dictionaries and the

cutoff value.

2. Saves the generated LCA scores tables to the specified Excel file.

3. Creates dot plots of the LCA scores and saves them in the Excel file.

4. Creates stacked bar charts of the LCA scores and appends them to the Excel file.

5. Prints the last row occupied in the Excel charts sheet, which indicates where the plots end.

Note:

• The dot_plots_xcl and stacked_bars_xcl functions are imported inside this function to

  avoid circular imports.

• The function relies on helper functions such as sector_lca_scores and

  sector_lca_scores_to_excel to generate and save LCA scores, and dot_plots_xcl and stacked_bars_xcl for generating plots.