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added lca_excel.py and select_values.py
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Nair, Rahul Ramesh
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Nov 6, 2024
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| """ | ||
| lca_excel.py | ||
| This module provides functions for exporting Life Cycle Assessment (LCA) calculation results | ||
| to an Excel format, with each result from an impact categort stored in a separate sheet, which is | ||
| a recurring theme in lca-related calculations. | ||
| Functions: | ||
| - clean_sheet_name(name, max_length=30): Cleans and truncates sheet names for Excel compatibility. | ||
| - result_to_excel(results, methods_of_interest, filename): Exports LCA results to an Excel file | ||
| with a final sheet summarizing impact assessment methods. | ||
| Dependencies: | ||
| - pandas | ||
| - openpyxl (since pandas does not automatically install this) | ||
| """ | ||
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| import re | ||
| import os | ||
| import pandas as pd | ||
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| def clean_sheet_name(name, max_length=30): | ||
| """Cleans and truncates a sheet name to make it compatible with Excel's requirements. | ||
| This function removes invalid characters, trims leading and trailing underscores, | ||
| and ensures that the name does not exceed the specified maximum length. | ||
| Args: | ||
| name (str): The original sheet name that may contain invalid characters. | ||
| max_length (int): Maximum allowed length for the sheet name. Defaults to 30. | ||
| Returns: | ||
| str: A cleaned and truncated version of the sheet name, compatible with Excel. | ||
| """ | ||
| # Remove invalid characters and trim whitespace, quotes, and brackets | ||
| name = re.sub( | ||
| r"[\\/*?:\[\]']", "_", name | ||
| ) # Replace invalid characters with underscores | ||
| name = re.sub(r"[(),]", "", name) # Remove parentheses and commas | ||
| name = name.strip("_") # Remove leading/trailing underscores | ||
| return name[:max_length] # Ensure the name does not exceed the maximum length | ||
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| def result_to_excel(results, methods_of_interest, filename): | ||
| """Exports LCA calculation results to an Excel file, with each result in a separate sheet. | ||
| This function takes a dictionary of LCA calculation results, each associated with a specific | ||
| impact assessment method, and writes them to separate sheets in an Excel file. Additionally, | ||
| it creates a final sheet with details of the impact assessment methods such as | ||
| method names, impact categories, and units. | ||
| Args: | ||
| results (dict): A dictionary where keys are method names (or other identifiers) and | ||
| values are pandas DataFrames containing LCA results for each method. | ||
| methods_of_interest (list of tuples): A list of tuples, each containing: | ||
| - Method Name (str): The name of the impact assessment method. | ||
| - Impact Category (str): The impact category associated with the method. | ||
| - Unit (str): The unit of measurement for the impact category. | ||
| filename (str): The path where the Excel file should be saved. | ||
| Returns: | ||
| str: The absolute path to the saved Excel file. | ||
| """ | ||
| impact_assessment_methods_df = pd.DataFrame( | ||
| methods_of_interest, | ||
| columns=["Method Name", "Impact category (i.e sheet name here)", "Unit"], | ||
| ) | ||
| with pd.ExcelWriter(filename) as writer: | ||
| for method, df in results.items(): | ||
| sheet_name = clean_sheet_name(str(method)) | ||
| df.to_excel(writer, sheet_name=sheet_name, index=False) | ||
| # Add impact assessment methods as the final sheet | ||
| impact_assessment_methods_df.to_excel( | ||
| writer, sheet_name="IA Methods", index=False | ||
| ) | ||
| file_path = os.path.abspath(filename) | ||
| print(f"Results written to {file_path}") |
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| """ | ||
| select_values.py | ||
| This module provides some functions for LCA workflows in Jupyter notebook | ||
| which require numerous user inputs such as selecting or changing impact assessment methods. | ||
| """ | ||
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| import ipywidgets as widgets | ||
| from IPython.display import display | ||
| import os | ||
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| def result_folders(folder_path, result_direcs): | ||
| """Creates folders for storing results if they do not already exist. | ||
| Args: | ||
| folder_path (str): The base folder path where result directories should be created. | ||
| In Windows, use double backslash to enter the path. Eg. "D:\\test" | ||
| result_direcs (list of str): A list of folder/directory names to create within the base folder. | ||
| Prints: | ||
| str: A message indicating the location of folders that were created. | ||
| """ | ||
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| if not os.path.exists(folder_path): | ||
| os.mkdir(folder_path) | ||
| print(f"Base folder created at: {os.path.abspath(folder_path)}") | ||
| else: | ||
| print(f"Base folder already exists at: {os.path.abspath(folder_path)}") | ||
| # creates directories for storing results with in the base folder. | ||
| for direc in result_direcs: | ||
| sub_folder_path = os.path.join(folder_path, direc) | ||
| if not os.path.exists(sub_folder_path): | ||
| os.mkdir(sub_folder_path) | ||
| print(f"Subfolder created at: {os.path.abspath(sub_folder_path)}") | ||
| else: | ||
| print(f"Subfolder already exists at: {os.path.abspath(sub_folder_path)}") | ||
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| def single_select_dropdown(names): | ||
| """Creates a single-selection dropdown widget for users to select an option. | ||
| Args: | ||
| names (list of str): A list of names/options to be displayed in the dropdown. | ||
| Returns: | ||
| dict: A dictionary containing the selected value with the key 'value'. | ||
| """ | ||
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| if not names: | ||
| raise ValueError("The 'names' list cannot be empty.") | ||
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| dropdown = widgets.Dropdown( | ||
| options=names, | ||
| value=names[0], # Default value | ||
| description="Assessment methodology:", | ||
| disabled=False, | ||
| ) | ||
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| # Display the dropdown widget | ||
| display(dropdown) | ||
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| # Variable to store the selected value | ||
| selected_value = {"value": dropdown.value} | ||
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| # Function to update the selected value | ||
| def on_change(change): | ||
| selected_value["value"] = change["new"] | ||
| print(f"You selected: {selected_value['value']}") | ||
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| # Attach the function to the dropdown | ||
| dropdown.observe(on_change, names="value") | ||
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| return selected_value | ||
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| def multi_select_dropdown(names): | ||
| """Creates a multi-selection dropdown widget for users to select multiple options. | ||
| Args: | ||
| names (list of str): A list of names/options to be displayed in the multi-select widget. | ||
| Returns: | ||
| dict: A dictionary containing the selected values with the key 'values'. | ||
| """ | ||
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| if not names: | ||
| raise ValueError("The 'names' list cannot be empty.") | ||
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| height_of_each_dropdown_item = ( | ||
| 20 # sets a default height in pixel for dropdown items | ||
| ) | ||
| dropdown_box_height = ( | ||
| len(names) * height_of_each_dropdown_item + 20 | ||
| ) # estimates the total height of the drop down window based on the total number of items. | ||
| multi_select = widgets.SelectMultiple( | ||
| options=names, | ||
| value=[], # Default value, an empty list | ||
| description="Impact Categories:", | ||
| disabled=False, | ||
| layout=widgets.Layout(width="100%", height=f"{dropdown_box_height}px"), | ||
| ) | ||
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| # Display the multiple selection widget | ||
| display(multi_select) | ||
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| # Variable to store the selected values | ||
| selected_values = {"values": multi_select.value} | ||
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| # Function to update the selected values | ||
| def on_change_multiple_selection(change): | ||
| selected_values["values"] = list(change["new"]) | ||
| print(f"You selected: {selected_values['values']}") | ||
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| # Attach the function to the multiple selection widget | ||
| multi_select.observe(on_change_multiple_selection, names="value") | ||
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| return selected_values |
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