+
+
+
++ Oops! It looks like you've stumbled upon a page that's been recycled into the digital abyss. + But don't worry, we're all about sustainability here. + Why not take a moment to reduce, reuse, and recycle your clicks by heading back to the main page? + And remember, every little bit counts in the grand scheme of things. +
+ + +
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+
+
+from pulpo.utils import optimizer, bw_parser, converter, saver
+from typing import List, Union
+import webbrowser
+from tests.rice_database import setup_rice_husk_db
+from tests.generic_database import setup_generic_db
+
+
+[docs]
+class PulpoOptimizer:
+ def __init__(self, project: str, database: Union[str, List[str]], method: Union[str, List[str], dict], directory: str):
+ """
+ Initializes the PulpoOptimizer with project, databases, method, and directory.
+
+ Args:
+ project (str): Name of the project.
+ database (Union[str, List[str]]): Name of the database or list of two databases
+ (e.g. foreground and linked background).
+ method (Union[str, List[str], dict]): Method(s) for optimization.
+ directory (str): Directory for saving results.
+ """
+ self.project = project
+ self.database = database
+ self.intervention_matrix = 'biosphere3'
+ self.method = converter.convert_to_dict(method)
+ self.directory = directory
+ self.lci_data = None
+ self.instance = None
+
+
+[docs]
+ def get_lci_data(self):
+ """
+ Imports LCI data for the project using the specified database and method.
+ """
+ self.lci_data = bw_parser.import_data(self.project, self.database, self.method, self.intervention_matrix)
+
+
+
+[docs]
+ def instantiate(self, choices={}, demand={}, upper_limit={}, lower_limit={}, upper_elem_limit={},
+ upper_imp_limit={}):
+ """
+ Combines inputs and instantiates the optimization model.
+
+ Args:
+ choices (dict): Choices for the model.
+ demand (dict): Demand data.
+ upper_limit (dict): Upper limit constraints.
+ lower_limit (dict): Lower limit constraints.
+ upper_elem_limit (dict): Upper elemental limit constraints.
+ upper_imp_limit (dict): Upper impact limit constraints.
+ """
+ # Instantiate only for those methods that are part of the objective or the limits
+ methods = {h: self.method[h] for h in self.method if self.method[h] != 0 or h in upper_imp_limit}
+ data = converter.combine_inputs(self.lci_data, demand, choices, upper_limit, lower_limit, upper_elem_limit,
+ upper_imp_limit, methods)
+ self.instance = optimizer.instantiate(data)
+
+
+
+[docs]
+ def solve(self, GAMS_PATH=False, solver_name=None, options=None):
+ """
+ Solves the optimization model and calculates additional methods and inventory flows if needed.
+
+ Args:
+ GAMS_PATH (bool): Path to GAMS if needed.
+ options (dict): Additional options for the solver.
+
+ Returns:
+ results: Results of the optimization.
+ """
+ results, self.instance = optimizer.solve_model(self.instance, GAMS_PATH, solver_name=solver_name, options=options)
+
+ # Post calculate additional methods, in case several methods have been specified and one of them is 0
+ if not isinstance(self.method, str):
+ if len(self.method) > 1 and 0 in [self.method[x] for x in self.method]:
+ self.instance = optimizer.calculate_methods(self.instance, self.lci_data, self.method)
+
+ self.instance = optimizer.calculate_inv_flows(self.instance, self.lci_data)
+ return results
+
+
+
+[docs]
+ def retrieve_processes(self, keys=None, processes=None, reference_products=None, locations=None):
+ """
+ Retrieves processes from the database based on given filters.
+
+ Args:
+ keys (list): List of keys to filter activities.
+ processes (list): List of processes to filter.
+ reference_products (list): List of reference products to filter.
+ locations (list): List of locations to filter.
+
+ Returns:
+ activities: Filtered activities from the database.
+ """
+ processes = bw_parser.retrieve_processes(self.project, self.database, keys, processes, reference_products,
+ locations)
+ return processes
+
+
+
+[docs]
+ def retrieve_activities(self, keys=None, activities=None, reference_products=None, locations=None):
+ """
+ Works the same as "retrieve_processes" but with a different name. Will be obsolete in future versions.
+ """
+ activities = bw_parser.retrieve_processes(self.project, self.database, keys, activities, reference_products,
+ locations)
+ return activities
+
+
+
+[docs]
+ def retrieve_envflows(self, keys=None, activities=None, categories=None):
+ """
+ Retrieves environmental flows from the database based on given filters.
+
+ Args:
+ keys (list): List of keys to filter environmental flows.
+ activities (list): List of activities to filter.
+ categories (list): List of categories to filter.
+
+ Returns:
+ activities: Filtered environmental flows from the database.
+ """
+ activities = bw_parser.retrieve_env_interventions(project=self.project,
+ intervention_matrix=self.intervention_matrix, keys=keys,
+ activities=activities, categories=categories)
+ return activities
+
+
+
+[docs]
+ def retrieve_methods(self, string=""):
+ """
+ Retrieves methods from the database based on a search string.
+
+ Args:
+ string (str): Search string for methods.
+
+ Returns:
+ methods: List of methods that match the search string.
+ """
+ methods = bw_parser.retrieve_methods(self.project, string)
+ return methods
+
+
+
+[docs]
+ def save_results(self, choices={}, constraints={}, demand={}, name='results.xlxs'):
+ """
+ Saves the results of the optimization to a file.
+
+ Args:
+ choices (dict): Choices for the model.
+ constraints (dict): Constraints applied during optimization.
+ demand (dict): Demand data used in optimization.
+ name (str): Name of the file to save results.
+ """
+ saver.save_results(self.instance, self.project, self.database, choices, constraints, demand,
+ self.lci_data['process_map'], self.lci_data['intervention_map'], self.directory, name)
+
+
+
+[docs]
+ def summarize_results(self, choices={}, constraints={}, demand={}, zeroes=False):
+ """
+ Summarizes the results of the optimization.
+
+ Args:
+ choices (dict): Choices for the model.
+ constraints (dict): Constraints applied during optimization.
+ demand (dict): Demand data used in optimization.
+ zeroes (bool): Whether to include zero values in the summary.
+ """
+ saver.summarize_results(self.instance, choices, constraints, demand,
+ self.lci_data['process_map'], zeroes)
+
+
+
+
+
+[docs]
+def electricity_showcase():
+ """
+ Opens the electricity showcase notebook in the web browser.
+ """
+ github_url = 'https://github.com/flechtenberg/pulpo/blob/master/notebooks/electricity_showcase.ipynb'
+ nbviewer_url = 'https://nbviewer.jupyter.org/github/' + github_url.split('github.com/')[1]
+ webbrowser.open(nbviewer_url)
+
+
+
+
+[docs]
+def hydrogen_showcase():
+ """
+ Opens the hydrogen showcase notebook in the web browser.
+ """
+ github_url = 'https://github.com/flechtenberg/pulpo/blob/master/notebooks/hydrogen_showcase.ipynb'
+ nbviewer_url = 'https://nbviewer.jupyter.org/github/' + github_url.split('github.com/')[1]
+ webbrowser.open(nbviewer_url)
+
+
+
+
+[docs]
+def plastic_showcase():
+ """
+ Opens the plastic showcase notebook in the web browser.
+ """
+ github_url = 'https://github.com/flechtenberg/pulpo/blob/master/notebooks/plastic_showcase.ipynb'
+ nbviewer_url = 'https://nbviewer.jupyter.org/github/' + github_url.split('github.com/')[1]
+ webbrowser.open(nbviewer_url)
+
+
+
+
+[docs]
+def install_rice_husk_db():
+ """
+ Sets up the rice husk example database.
+ """
+ setup_rice_husk_db()
+
+
+
+[docs]
+def install_generic_db(project="generic_db_project", database="generic_db", n_prod=5, n_proc=3, n_reg=3, n_inputs=4, n_flows=4, n_methods=2, seed=None, return_data=False):
+ """
+ Sets up the generic LCI database in Brightway2 with specified parameters.
+
+ Args:
+ project (str): Name of the Brightway2 project to create or use. Defaults to "generic_db_project".
+ database (str): Name of the database to create or use. Defaults to "generic_db".
+ n_prod (int): Number of products to generate. Defaults to 5.
+ n_proc (int): Maximum number of processes per product. Defaults to 3.
+ n_reg (int): Number of regions where processes can be active. Defaults to 3.
+ n_inputs (int): Maximum number of inputs per process. Defaults to 4.
+ n_flows (int): Number of environmental flows to generate. Defaults to 4.
+ n_methods (int): Number of impact assessment methods to create. Defaults to 2.
+ seed (int, optional): Seed for reproducibility of random data generation. Defaults to None.
+ return_data (bool): If True, returns the generated matrices (technosphere, biosphere, and
+ characterization). Defaults to False.
+
+ Returns:
+ tuple: If `return_data` is True, returns a tuple containing:
+ - technosphere_matrix (np.ndarray): The technosphere matrix.
+ - biosphere_matrix (np.ndarray): The biosphere matrix.
+ - characterization_matrices (dict): A dictionary of characterization factor matrices.
+ """
+ return setup_generic_db(project=project, database=database, n_prod=n_prod, n_proc=n_proc, n_reg=n_reg,
+ n_inputs=n_inputs, n_flows=n_flows, n_methods=n_methods,
+ seed=seed, return_data=return_data)
+
+
+
+
+
+
+
+from typing import List, Union, Dict, Any
+import bw2calc as bc
+import bw2data as bd
+from pulpo.utils.utils import is_bw25
+
+
+[docs]
+def import_data(project: str, databases: Union[str, List[str]], method: Union[str, List[str], Dict[str, int]],
+ intervention_matrix_name: str) -> Dict[str, Union[dict, Any]]:
+ """
+ Main function to import LCI data for a project from one or more databases.
+
+ Args:
+ project (str): Name of the project.
+ databases (Union[str, List[str]]): Name of the primary database or a list of databases
+ (foreground, background).
+ method (Union[str, List[str], Dict[str, int]]): Method(s) for data retrieval.
+ intervention_matrix_name (str): Name of the intervention matrix.
+
+ Returns:
+ Dict[str, Union[dict, Any]]: Dictionary containing imported LCI data.
+ """
+
+ # Set project and check if it exists
+ if project not in bd.projects:
+ raise ValueError(f"Project '{project}' does not exist. Please check the project name.")
+
+ bd.projects.set_current(project)
+
+ # Normalize databases input to a list
+ if isinstance(databases, str):
+ databases = [databases]
+
+ # Validate each specified database
+ for db in databases:
+ if db not in bd.databases:
+ raise ValueError(
+ f"Database '{db}' does not exist in the project '{project}'. "
+ f"Available databases: {list(bd.databases.keys())}"
+ )
+
+ # Prepare methods
+ if isinstance(method, str):
+ method = [method] # Convert single string to list of strings
+ method = sorted(method)
+
+ # Retrieve and validate methods
+ methods = retrieve_methods(project, method)
+ invalid_methods = [m for m in method if m not in [str(mt) for mt in bd.methods]]
+ if invalid_methods:
+ raise ValueError(
+ f"The following methods do not exist in the project '{project}': {invalid_methods}. "
+ f"Available methods: {[str(mt) for mt in bd.methods]}"
+ )
+
+ # Initialize database objects
+ eidb = bd.Database(databases[0]) # Foreground database
+ eidb_secondary = bd.Database(databases[1]) if len(databases) > 1 else None # Secondary background database
+
+ rand_act = eidb.random() # Random activity from foreground database
+
+ bw25 = is_bw25()
+ characterization_matrices = {}
+ secondary_lca = None # Placeholder for secondary LCA object
+
+ if bw25:
+ # Process with bw25 logic for foreground database
+ functional_units_1 = {"act1": {rand_act.id: 1}}
+ config_1 = {"impact_categories": methods}
+ data_objs_1 = bd.get_multilca_data_objs(functional_units=functional_units_1, method_config=config_1)
+
+ lca = bc.MultiLCA(demands=functional_units_1, method_config=config_1, data_objs=data_objs_1)
+ lca.load_lci_data()
+ lca.load_lcia_data()
+
+ # Store the characterization matrices for methods
+ characterization_matrices = {str(method): lca.characterization_matrices[method] for method in methods}
+
+ # Perform LCA for the secondary database, if specified
+ if eidb_secondary:
+ functional_units_2 = {"act2": {eidb_secondary.random().id: 1}}
+ config_2 = {"impact_categories": methods}
+ data_objs_2 = bd.get_multilca_data_objs(functional_units=functional_units_2, method_config=config_2)
+
+ secondary_lca = bc.MultiLCA(demands=functional_units_2, method_config=config_2, data_objs=data_objs_2)
+ secondary_lca.load_lci_data() # Only need the product dictionary
+ # secondary_lca.products_dict will be available later
+
+ else:
+ # Process with bw2 logic for foreground database
+ for method in methods:
+ lca = bc.LCA({rand_act: 1}, method)
+ lca.lci()
+ lca.lcia()
+ characterization_matrices[str(method)] = lca.characterization_matrix
+
+ # Perform LCA for the secondary database, if specified
+ if eidb_secondary:
+ rand_act_bg = eidb_secondary.random()
+ for method in methods:
+ secondary_lca = bc.LCA({rand_act_bg: 1}, method)
+ secondary_lca.lci() # Load LCI to access product dictionary
+ # Stop after loading one method since we're only interested in secondary_lca.products_dict
+ break
+
+ # Extract A (Technosphere) and B (Biosphere) matrices from the LCA
+ technology_matrix = lca.technosphere_matrix # A matrix
+ intervention_matrix = lca.biosphere_matrix # B matrix
+
+ # Initialize the process map
+ process_map = {}
+
+ if bw25:
+ # Create activity map for the primary database (eidb)
+ process_map.update({act.key: lca.dicts.product[act.id] for act in eidb})
+
+ # Add secondary database (secondary_eidb) activities, if available
+ if eidb_secondary and secondary_lca:
+ process_map.update({act.key: secondary_lca.dicts.product[act.id] for act in eidb_secondary})
+ else:
+ # Use product_dict for the primary database (eidb)
+ process_map.update(lca.product_dict)
+
+ # Add secondary database (secondary_eidb) product_dict, if available
+ if eidb_secondary and secondary_lca:
+ process_map.update(secondary_lca.product_dict)
+
+ # Add descriptive strings to the process map for both primary and secondary databases
+ for act in eidb:
+ process_map[process_map[act.key]] = (
+ f"{act['name']} | {act.get('reference product', '')} | {act.get('location', '')}"
+ )
+
+ if eidb_secondary:
+ for act in eidb_secondary:
+ process_map[process_map[act.key]] = (
+ f"{act['name']} | {act.get('reference product', '')} | {act.get('location', '')}"
+ )
+
+ if intervention_matrix_name in bd.databases:
+ eidb_bio = bd.Database(intervention_matrix_name)
+ if bw25:
+ intervention_map = {act.key: lca.dicts.biosphere[act.id] for act in eidb_bio if act.id in lca.dicts.biosphere} # TODO: This is adherring to old ways of storring data with keys ... how to work with IDs instead?
+ else:
+ intervention_map = lca.biosphere_dict
+ for act in eidb_bio:
+ if act.key in intervention_map:
+ intervention_map[intervention_map[act.key]] = act['name'] + ' | ' + str(act['categories'])
+ else:
+ print(
+ "The name of the biosphere is not '" + intervention_matrix_name + "'. Please specify the correct biosphere.")
+ return {}
+
+ lci_data = {
+ 'matrices': characterization_matrices,
+ 'intervention_matrix': intervention_matrix,
+ 'technology_matrix': technology_matrix,
+ 'process_map': process_map,
+ 'intervention_map': intervention_map,
+ }
+
+ return lci_data
+
+
+
+
+[docs]
+def retrieve_processes(project: str, databases: Union[str, List[str]], keys=None, activities=None,
+ reference_products=None, locations=None):
+ """
+ Retrieve activities from one or more databases based on specified keys, activities, reference products, and locations.
+
+ Args:
+ project (str): Name of the project.
+ databases (Union[str, List[str]]): Name of the primary database or a list of databases (foreground, background).
+ keys (list, optional): List of keys to filter activities.
+ activities (list, optional): List of activity names to filter.
+ reference_products (list, optional): List of reference products to filter.
+ locations (list, optional): List of locations to filter.
+
+ Returns:
+ list: List of matching activities from the specified databases.
+ """
+ # Set project
+ bd.projects.set_current(project)
+
+ # Normalize databases to a list
+ if isinstance(databases, str):
+ databases = [databases]
+
+ # Ensure filters are lists
+ if activities is not None and not isinstance(activities, list):
+ activities = [activities]
+ if reference_products is not None and not isinstance(reference_products, list):
+ reference_products = [reference_products]
+ if locations is not None and not isinstance(locations, list):
+ locations = [locations]
+
+ matching_processes = []
+
+ # Search each database
+ for db_name in databases:
+ eidb = bd.Database(db_name)
+
+ # Preprocess keys for fast lookup if provided
+ if keys is not None:
+ if isinstance(keys, str):
+ keys = [keys]
+ keys_set = set(eval(key) for key in keys) # Use a set for faster lookup
+ matching_processes.extend([proc for proc in eidb if proc.key in keys_set])
+ else:
+ # Preprocess filters for fast lookup
+ activity_set = set(activities) if activities is not None else None
+ reference_product_set = set(reference_products) if reference_products is not None else None
+ location_set = set(locations) if locations is not None else None
+
+ # Filter processes efficiently
+ matching_processes.extend([
+ proc for proc in eidb
+ if (activity_set is None or proc['name'] in activity_set) and
+ (reference_product_set is None or proc['reference product'] in reference_product_set) and
+ (location_set is None or proc['location'] in location_set)
+ ])
+
+ if not matching_processes:
+ print("No activities match the given specifications or the input format is incorrect.")
+ return matching_processes
+
+
+
+
+[docs]
+def retrieve_env_interventions(project: str = '', intervention_matrix: str = 'biosphere3', keys=None, activities=None,
+ categories=None):
+ """
+ Retrieve environmental flows from the biosphere database based on specified keys, activities, and categories.
+
+ Args:
+ project (str, optional): Name of the project.
+ intervention_matrix (str): Name of the intervention matrix.
+ keys (list, optional): List of keys to filter environmental flows.
+ activities (list, optional): List of activity names to filter.
+ categories (list, optional): List of categories to filter.
+
+ Returns:
+ list: List of matching environmental flows from the database.
+ """
+
+ # Set project and get database
+ bd.projects.set_current(project)
+ eidb = bd.Database(intervention_matrix)
+
+ # Filter by keys if provided
+ if keys is not None:
+ if isinstance(keys, str):
+ keys = [keys]
+ keys = [eval(key) for key in keys]
+ return [flow for flow in eidb if flow.key in keys]
+
+ matching_flows = []
+
+ # Filter by activities and categories
+ for flow in eidb:
+ if (activities is None or flow['name'] in activities) and \
+ (categories is None or str(flow['categories']) in categories):
+ matching_flows.append(flow)
+
+ if not matching_flows:
+ print("No flows match the given specifications or the input format is incorrect.")
+ else:
+ return matching_flows
+
+
+
+
+[docs]
+def retrieve_methods(project: str, sub_string: List[str]) -> List[str]:
+ """
+ Retrieve all methods that contain the specified list of substrings.
+
+ Args:
+ project (str): Name of the project.
+ sub_string (List[str]): List of substrings to search for in method names.
+
+ Returns:
+ List[str]: List of methods that match the substrings.
+ """
+ bd.projects.set_current(project)
+ return [method for method in bd.methods if any([x.lower() in str(method).lower() for x in sub_string])]
+
+
+#if __name__ == '__main__':
+# if is_bw25():
+# project = "pulpo_bw25"
+# biosphere = "ecoinvent-3.8-biosphere"
+# methods = {"('ecoinvent-3.8', 'IPCC 2013', 'climate change', 'GWP 100a')": 1,
+# "('ecoinvent-3.8', 'IPCC 2013', 'climate change', 'GWP 20a')": 0,
+# }
+# else:
+# project = "pulpo"
+# biosphere = "biosphere3"
+# methods = {"('IPCC 2013', 'climate change', 'GWP 100a')": 1,
+# "('IPCC 2013', 'climate change', 'GWP 20a')": 0,
+# }
+#
+# database = "ecoinvent-3.8-cutoff"
+#
+#
+# import_data(project, database, methods, intervention_matrix_name=biosphere)
+
+
+
+
+
+import scipy.sparse as sparse
+
+
+
+[docs]
+def combine_inputs(lci_data, demand, choices, upper_limit, lower_limit, upper_inv_limit, upper_imp_limit, methods):
+ """
+ Combines all the inputs into a dictionary as an input for the optimization model.
+
+ Args:
+ lci_data (dict): LCI data containing matrices and mappings.
+ demand (dict): Demand data.
+ choices (dict): Choices for the model.
+ upper_limit (dict): Upper limit constraints.
+ lower_limit (dict): Lower limit constraints.
+ upper_inv_limit (dict): Upper intervention limit constraints.
+ upper_imp_limit (dict): Upper impact limit constraints.
+ methods (dict): Methods for environmental impact assessment.
+
+ Returns:
+ dict: Combined data dictionary for the optimization model.
+ """
+
+ # Load LCI data matrices and mappings
+ matrices = lci_data['matrices']
+ intervention_matrix = lci_data['intervention_matrix']
+ technology_matrix = lci_data['technology_matrix']
+ process_map = lci_data['process_map']
+ intervention_map = lci_data['intervention_map']
+
+ # Remove matrices that are not part of the objective
+ matrices = {h: matrices[h] for h in matrices if str(h) in methods}
+
+ # Prepare the environmental impact matrices Q[h]*B
+ env_cost = {h: sparse.csr_matrix.dot(matrices[h], intervention_matrix) for h in matrices}
+
+ # Convert sparse csr biosphere impact matrix to dictionary
+ env_cost_dict = {(i - 1, env_cost[h].indices[j], h): env_cost[h].data[j]
+ for h in matrices for i in range(1, intervention_matrix.shape[0] + 1)
+ for j in range(env_cost[h].indptr[i - 1], env_cost[h].indptr[i])}
+
+ # Convert sparse csr technology matrix to dictionary
+ technology_matrix_dict = {(i - 1, technology_matrix.indices[j]): technology_matrix.data[j]
+ for i in range(1, technology_matrix.shape[0] + 1)
+ for j in range(technology_matrix.indptr[i - 1], technology_matrix.indptr[i])}
+
+ # Convert sparse csr intervention flow matrix to dictionary
+ inv_to_consider = [intervention_map[g] for g in upper_inv_limit]
+ inv_dict = {(g, intervention_matrix.indices[j]): intervention_matrix.data[j]
+ for g in inv_to_consider
+ for j in range(intervention_matrix.indptr[g], intervention_matrix.indptr[g + 1])}
+
+ # Make technology matrix rectangular and update keys and product_ids
+ keys = {} # Store the activity keys of the choices
+ product_ids = [] # Store the product IDs of the choices
+ for key, processes in choices.items():
+ for proc in processes:
+ product_id = process_map[proc.key]
+ keys[product_id] = key
+ product_ids.append(product_id)
+
+ for product, process in list(technology_matrix_dict):
+ if product in product_ids:
+ if (keys[product], process) not in technology_matrix_dict:
+ technology_matrix_dict[(keys[product], process)] = technology_matrix_dict[(product, process)]
+ else:
+ technology_matrix_dict[(keys[product], process)] += technology_matrix_dict[(product, process)]
+ del technology_matrix_dict[(product, process)]
+
+ # Create sets using set comprehensions for better performance
+ PRODUCTS = {None: list({i[0] for i in technology_matrix_dict})}
+ PROCESS = {None: list({i[1] for i in technology_matrix_dict})}
+ PRODUCT_PROCESS = {None: list({(i[0], i[1]) for i in technology_matrix_dict})}
+ ENV_COST = {None: list({i[0] for i in env_cost_dict})}
+ ENV_COST_PROCESS = {None: list({i for i in env_cost_dict})}
+ INV = {None: list({i[0] for i in inv_dict})}
+ INV_PROCESS = {None: list({(i[0], i[1]) for i in inv_dict})}
+ INDICATOR = {None: list({h for h in matrices})}
+
+ # Specify the demand
+ demand_dict = {prod: 0 for prod in PRODUCTS[None]}
+ for dem in demand:
+ if dem in process_map:
+ # Case when dem is a key in process_map
+ demand_dict[process_map[dem]] = demand[dem]
+ elif dem in choices:
+ # Case when dem is already one of the elements in process_map
+ demand_dict[dem] = demand[dem]
+ else:
+ # Case when dem is neither a key nor a value in process_map
+ raise ValueError(f"'{dem}' is not found in process_map keys or values.")
+
+ # Specify the lower limit
+ lower_limit_dict = {proc: -1e20 for proc in PROCESS[None]}
+ for choice in choices:
+ for proc in choices[choice]:
+ lower_limit_dict[process_map[proc]] = 0
+ for proc in lower_limit:
+ lower_limit_dict[process_map[proc]] = lower_limit[proc]
+
+ # Specify the upper limit
+ upper_limit_dict = {proc: 1e20 for proc in PROCESS[None]}
+ for proc in upper_limit:
+ upper_limit_dict[process_map[proc]] = upper_limit[proc]
+ for choice in choices:
+ for proc in choices[choice]:
+ upper_limit_dict[process_map[proc]] = choices[choice][proc]
+
+ # Check if a supply has been specified
+ supply_dict = {prod: 0 for prod in PRODUCTS[None]}
+ for proc in list(lower_limit.keys() & upper_limit.keys()):
+ supply_dict[process_map[proc]] = 1 if lower_limit[proc] == upper_limit[proc] else 0
+
+ # Specify the upper elementary flow limit
+ upper_inv_limit_dict = {elem: 1e24 for elem in INV[None]}
+ for inv in upper_inv_limit:
+ upper_inv_limit_dict[intervention_map[inv.key]] = upper_inv_limit[inv]
+
+ # Specify the upper impact category limit
+ upper_imp_limit_dict = {imp: 1e24 for imp in INDICATOR[None]}
+ for imp in upper_imp_limit:
+ upper_imp_limit_dict[imp] = upper_imp_limit[imp]
+
+ # Create weights
+ weights = {method: 1 for method in matrices} if methods == {} else methods
+
+ # Assemble the final data dictionary
+ model_data = {
+ None: {
+ 'PRODUCT': PRODUCTS,
+ 'PROCESS': PROCESS,
+ 'ENV_COST': ENV_COST,
+ 'INDICATOR': INDICATOR,
+ 'INV': INV,
+ 'PRODUCT_PROCESS': PRODUCT_PROCESS,
+ 'ENV_COST_PROCESS': ENV_COST_PROCESS,
+ 'INV_PROCESS': INV_PROCESS,
+ 'TECH_MATRIX': technology_matrix_dict,
+ 'ENV_COST_MATRIX': env_cost_dict,
+ 'INV_MATRIX': inv_dict,
+ 'FINAL_DEMAND': demand_dict,
+ 'SUPPLY': supply_dict,
+ 'LOWER_LIMIT': lower_limit_dict,
+ 'UPPER_LIMIT': upper_limit_dict,
+ 'UPPER_INV_LIMIT': upper_inv_limit_dict,
+ 'UPPER_IMP_LIMIT': upper_imp_limit_dict,
+ 'WEIGHTS': weights,
+ }
+ }
+ return model_data
+
+
+
+
+[docs]
+def convert_to_dict(input_data):
+ """
+ Checks if the passed method is in the correct format and converts it accordingly.
+
+ Args:
+ input_data (Union[str, list, dict]): Input data to be converted.
+
+ Returns:
+ dict: Converted dictionary with methods and their weights.
+
+ Raises:
+ ValueError: If the input data is not in the expected format.
+ """
+
+ # Check if the input is a string
+ if isinstance(input_data, str):
+ return {input_data: 1}
+
+ # Check if the input is a list of strings
+ elif isinstance(input_data, list) and all(isinstance(item, str) for item in input_data):
+ return {item: 1 for item in input_data}
+
+ # Check if the input is a dictionary with numerical values
+ elif isinstance(input_data, dict) and all(isinstance(value, (int, float)) for value in input_data.values()):
+ return input_data
+
+ # If the input is of any other type, raise an error
+ else:
+ raise ValueError(
+ "Input should be either a string, a list of strings, or a dictionary with string indices and numerical values")
+
+
+
+
+
+
+import pyomo.environ as pyo
+from pyomo.contrib import appsi
+
+
+[docs]
+def create_model():
+ """
+ Builds an abstract model on top of the ecoinvent database.
+
+ Returns:
+ AbstractModel: The Pyomo abstract model for optimization.
+ """
+ model = pyo.AbstractModel()
+
+ # Sets
+ model.PRODUCT = pyo.Set(doc='Set of intermediate products (or technosphere exchanges), indexed by i')
+ model.PROCESS = pyo.Set(doc='Set of processes (or activities), indexed by j')
+ model.ENV_COST = pyo.Set(doc='Set of environmental cost flows, indexed by e')
+ model.INDICATOR = pyo.Set(doc='Set of impact assessment indicators, indexed by h')
+ model.INV = pyo.Set(doc='Set of intervention flows, indexed by g')
+ model.ENV_COST_PROCESS = pyo.Set(within=model.ENV_COST * model.PROCESS * model.INDICATOR, doc='Relation set between environmental cost flows and processes')
+ model.ENV_COST_IN = pyo.Set(model.INDICATOR, within=model.ENV_COST)
+ model.ENV_COST_OUT = pyo.Set(model.ENV_COST * model.INDICATOR, within=model.PROCESS)
+ model.PROCESS_IN = pyo.Set(model.PROCESS, within=model.PRODUCT)
+ model.PROCESS_OUT = pyo.Set(model.PRODUCT, within=model.PROCESS)
+ model.PRODUCT_PROCESS = pyo.Set(within=model.PRODUCT * model.PROCESS, doc='Relation set between intermediate products and processes')
+ model.INV_PROCESS = pyo.Set(within=model.INV * model.PROCESS, doc='Relation set between environmental flows and processes')
+ model.INV_OUT = pyo.Set(model.INV, within=model.PROCESS)
+
+ # Parameters
+ model.UPPER_LIMIT = pyo.Param(model.PROCESS, mutable=True, within=pyo.Reals, doc='Maximum production capacity of process j')
+ model.LOWER_LIMIT = pyo.Param(model.PROCESS, mutable=True, within=pyo.Reals, doc='Minimum production capacity of process j')
+ model.UPPER_INV_LIMIT = pyo.Param(model.INV, mutable=True, within=pyo.Reals, doc='Maximum intervention flow g')
+ model.UPPER_IMP_LIMIT = pyo.Param(model.INDICATOR, mutable=True, within=pyo.Reals, doc='Maximum impact on category h')
+ model.ENV_COST_MATRIX = pyo.Param(model.ENV_COST_PROCESS, mutable=True, doc='Enviornmental cost matrix Q*B describing the environmental cost flows e associated to process j')
+ model.INV_MATRIX = pyo.Param(model.INV_PROCESS, mutable=True, doc='Intervention matrix B describing the intervention flow g entering/leaving process j')
+ model.FINAL_DEMAND = pyo.Param(model.PRODUCT, mutable=True, within=pyo.Reals, doc='Final demand of intermediate product flows (i.e., functional unit)')
+ model.SUPPLY = pyo.Param(model.PRODUCT, mutable=True, within=pyo.Binary, doc='Binary parameter which specifies whether or not a supply has been specified instead of a demand')
+ model.TECH_MATRIX = pyo.Param(model.PRODUCT_PROCESS, mutable=True, doc='Technology matrix A describing the intermediate product i produced/absorbed by process j')
+ model.WEIGHTS = pyo.Param(model.INDICATOR, mutable=True, within=pyo.NonNegativeReals, doc='Weighting factors for the impact assessment indicators in the objective function')
+
+ # Variables
+ model.impacts = pyo.Var(model.INDICATOR, bounds=(-1e24, 1e24), doc='Environmental impact on indicator h evaluated with the established LCIA method')
+ model.scaling_vector = pyo.Var(model.PROCESS, bounds=(-1e24, 1e24), doc='Activity level of each process to meet the final demand')
+ model.inv_vector = pyo.Var(model.INV, bounds=(-1e24, 1e24), doc='Intervention flows')
+ model.slack = pyo.Var(model.PRODUCT, bounds=(-1e24, 1e24), doc='Supply slack variables')
+
+ # Building rules for sets
+ model.Env_in_out = pyo.BuildAction(rule=populate_env)
+ model.Process_in_out = pyo.BuildAction(rule=populate_in_and_out)
+ model.Inv_in_out = pyo.BuildAction(rule=populate_inv)
+
+ # Constraints
+ model.FINAL_DEMAND_CNSTR = pyo.Constraint(model.PRODUCT, rule=demand_constraint)
+ model.IMPACTS_CNSTR = pyo.Constraint(model.INDICATOR, rule=impact_constraint)
+ model.INVENTORY_CNSTR = pyo.Constraint(model.INV, rule=inventory_constraint)
+ model.UPPER_CNSTR = pyo.Constraint(model.PROCESS, rule=upper_constraint)
+ model.LOWER_CNSTR = pyo.Constraint(model.PROCESS, rule=lower_constraint)
+ model.SLACK_UPPER_CNSTR = pyo.Constraint(model.PRODUCT, rule=slack_upper_constraint)
+ model.SLACK_LOWER_CNSTR = pyo.Constraint(model.PRODUCT, rule=slack_lower_constraint)
+ model.INV_CNSTR = pyo.Constraint(model.INV, rule=upper_env_constraint)
+ model.IMP_CNSTR = pyo.Constraint(model.INDICATOR, rule=upper_imp_constraint)
+
+ # Objective function
+ model.OBJ = pyo.Objective(sense=pyo.minimize, rule=objective_function)
+
+ return model
+
+
+
+# Rule functions
+
+[docs]
+def populate_env(model):
+ """Relates the environmental flows to the processes."""
+ for i, j, h in model.ENV_COST_PROCESS:
+ if i not in model.ENV_COST_IN[h]:
+ model.ENV_COST_IN[h].add(i)
+ model.ENV_COST_OUT[i, h].add(j)
+
+
+
+
+[docs]
+def populate_in_and_out(model):
+ """Relates the inputs of an activity to its outputs."""
+ for i, j in model.PRODUCT_PROCESS:
+ model.PROCESS_OUT[i].add(j)
+ model.PROCESS_IN[j].add(i)
+
+
+
+[docs]
+def populate_inv(model):
+ """Relates the impacts to the environmental flows"""
+ for a, j in model.INV_PROCESS:
+ model.INV_OUT[a].add(j)
+
+
+
+[docs]
+def demand_constraint(model, i):
+ """Fixes a value in the demand vector"""
+ return sum(model.TECH_MATRIX[i, j] * model.scaling_vector[j] for j in model.PROCESS_OUT[i]) == model.FINAL_DEMAND[i] + model.slack[i]
+
+
+
+[docs]
+def impact_constraint(model, h):
+ """Calculates all the impact categories"""
+ return model.impacts[h] == sum(sum(model.ENV_COST_MATRIX[i, j, h] * model.scaling_vector[j] for j in model.ENV_COST_OUT[i, h]) for i in model.ENV_COST_IN[h])
+
+
+
+[docs]
+def inventory_constraint(model, g):
+ """Calculates the environmental flows"""
+ return model.inv_vector[g] == sum(model.INV_MATRIX[g, j] * model.scaling_vector[j] for j in model.INV_OUT[g])
+
+
+
+[docs]
+def upper_constraint(model, j):
+ """Ensures that variables are within capacities (Maximum production constraint) """
+ return model.scaling_vector[j] <= model.UPPER_LIMIT[j]
+
+
+
+[docs]
+def lower_constraint(model, j):
+ """ Minimum production constraint """
+ return model.scaling_vector[j] >= model.LOWER_LIMIT[j]
+
+
+
+[docs]
+def upper_env_constraint(model, g):
+ """Ensures that variables are within capacities (Maximum production constraint) """
+ return model.inv_vector[g] <= model.UPPER_INV_LIMIT[g]
+
+
+
+[docs]
+def upper_imp_constraint(model, h):
+ """ Imposes upper limits on selected impact categories """
+ return model.impacts[h] <= model.UPPER_IMP_LIMIT[h]
+
+
+
+
+[docs]
+def slack_upper_constraint(model, j):
+ """ Slack variable upper limit for activities where supply is specified instead of demand """
+ return model.slack[j] <= 1e20 * model.SUPPLY[j]
+
+
+
+[docs]
+def slack_lower_constraint(model, j):
+ """ Slack variable upper limit for activities where supply is specified instead of demand """
+ return model.slack[j] >= -1e20 * model.SUPPLY[j]
+
+
+
+[docs]
+def objective_function(model):
+ """Objective is a sum over all indicators with weights. Typically, the indicator of study has weight 1, the rest 0"""
+ return sum(model.impacts[h] * model.WEIGHTS[h] for h in model.INDICATOR)
+
+
+
+[docs]
+def calculate_methods(instance, lci_data, methods):
+ """
+ Calculates the impacts if a method with weight 0 has been specified.
+
+ Args:
+ instance: The Pyomo model instance.
+ lci_data (dict): LCI data containing matrices and mappings.
+ methods (dict): Methods for environmental impact assessment.
+
+ Returns:
+ instance: The updated Pyomo model instance with calculated impacts.
+ """
+ import scipy.sparse as sparse
+ import numpy as np
+ matrices = lci_data['matrices']
+ intervention_matrix = lci_data['intervention_matrix']
+ matrices = {h: matrices[h] for h in matrices if str(h) in methods}
+ env_cost = {h: sparse.csr_matrix.dot(matrices[h], intervention_matrix) for h in matrices}
+ try:
+ scaling_vector = np.array([instance.scaling_vector[x].value for x in instance.scaling_vector])
+ except:
+ scaling_vector = np.array([instance.scaling_vector[x] for x in instance.scaling_vector])
+
+ impacts = {}
+ for h in matrices:
+ impacts[h] = sum(env_cost[h].dot(scaling_vector))
+
+ # Check if instance.impacts_calculated exists
+ if hasattr(instance, 'impacts_calculated'):
+ # Update values if it already exists
+ for h in impacts:
+ instance.impacts_calculated[h].value = impacts[h]
+ else:
+ # Create instance.impacts_calculated
+ instance.impacts_calculated = pyo.Var([h for h in impacts], initialize=impacts)
+ return instance
+
+
+
+[docs]
+def calculate_inv_flows(instance, lci_data):
+ """
+ Calculates elementary flows post-optimization.
+
+ Args:
+ instance: The Pyomo model instance.
+ lci_data (dict): LCI data containing matrices and mappings.
+
+ Returns:
+ instance: The updated Pyomo model instance with calculated intervention flows.
+ """
+ import numpy as np
+ intervention_matrix = lci_data['intervention_matrix']
+ try:
+ scaling_vector = np.array([instance.scaling_vector[x].value for x in instance.scaling_vector])
+ except:
+ scaling_vector = np.array([instance.scaling_vector[x] for x in instance.scaling_vector])
+ flows = intervention_matrix.dot(scaling_vector)
+ # Check if inv_flows already exists in the model
+ # @TODO: Consider adding "inv_flows" directly as variable to the model and skip this check.
+ if hasattr(instance, 'inv_flows'):
+ # Update the values of the existing variable
+ for i in range(intervention_matrix.shape[0]):
+ instance.inv_flows[i].value = flows[i]
+ else:
+ # Create the variable if it does not exist
+ instance.inv_flows = pyo.Var(range(0, intervention_matrix.shape[0]), initialize=dict(enumerate(flows)))
+ return instance
+
+
+
+
+
+[docs]
+def instantiate(model_data):
+ """
+ Builds an instance of the optimization model with specific data and objective function.
+
+ Args:
+ model_data (dict): Data dictionary for the optimization model.
+
+ Returns:
+ ConcreteModel: The instantiated Pyomo model.
+ """
+ print('Creating Instance')
+ model = create_model()
+ problem = model.create_instance(model_data, report_timing=False)
+ print('Instance created')
+ return problem
+
+
+
+
+[docs]
+def solve_model(model_instance, gams_path=None, solver_name=None, options=None):
+ """
+ Solves the instance of the optimization model.
+
+ Args:
+ model_instance (ConcreteModel): The Pyomo model instance.
+ gams_path (str, optional): Path to the GAMS solver. If None, GAMS will not be used.
+ solver_name (str, optional): The solver to use ('highs', 'gams', or 'ipopt'). Defaults to 'highs' unless gams_path is provided.
+ options (list, optional): Additional options for the solver.
+
+ Returns:
+ tuple: Results of the optimization and the updated model instance.
+ """
+ results = None
+
+ # Use GAMS if gams_path is specified
+ if gams_path and (solver_name is None or solver_name.lower() == 'gams'):
+ pyo.pyomo.common.Executable('gams').set_path(gams_path)
+ solver = pyo.SolverFactory('gams')
+ print('GAMS solvers library availability:', solver.available())
+ print('Solver path:', solver.executable())
+
+ io_options = {
+ 'mtype': 'lp', # Type of problem (lp, nlp, mip, minlp)
+ 'solver': 'CPLEX', # Name of solver
+ }
+
+ if options is None:
+ options = [
+ 'option optcr = 1e-15;',
+ 'option reslim = 3600;',
+ 'GAMS_MODEL.optfile = 1;',
+ '$onecho > cplex.opt',
+ 'workmem=4096',
+ 'scaind=1',
+ #'numericalemphasis=1',
+ #'epmrk=0.99',
+ #'eprhs=1E-9',
+ '$offecho',
+ ]
+
+ results = solver.solve(
+ model_instance,
+ keepfiles=True,
+ symbolic_solver_labels=True,
+ tee=False,
+ report_timing=False,
+ io_options=io_options,
+ add_options=options
+ )
+
+ model_instance.solutions.load_from(results)
+
+ # Use IPOPT if explicitly specified
+ elif solver_name and solver_name.lower() == 'ipopt':
+ opt = pyo.SolverFactory('ipopt')
+ if options:
+ for option in options:
+ opt.options[option] = True
+ results = opt.solve(model_instance)
+
+ # Default to HiGHS if no solver specified or if solver_name is 'highs'
+ else:
+ opt = appsi.solvers.Highs()
+ results = opt.solve(model_instance)
+
+ return results, model_instance
+
+
+ print('Optimization problem solved')
+ ## TODO: Add a check for infeasibility and other solver errors
+
+ return results, model_instance
+
+
+
+
+
+
+import pyomo.environ as pyo
+from pyomo.repn.plugins.baron_writer import *
+import pandas as pd
+from pathlib import Path
+from IPython.display import display
+
+
+[docs]
+def save_results(instance, project, database, choices, constraints, demand, process_map, itervention_map, directory, name):
+ """
+ Saves the results of a Pyomo optimization model to an Excel file.
+
+ Args:
+ instance: The Pyomo model instance.
+ project (str): Name of the project.
+ database (str): Name of the database.
+ choices (dict): Choices for the model.
+ constraints (dict): Constraints applied during optimization.
+ demand (dict): Demand data used in optimization.
+ process_map (dict): Mapping of process IDs to descriptions.
+ itervention_map (dict): Mapping of intervention IDs to descriptions.
+ directory (str): Directory to save the results file.
+ name (str): Name of the results file.
+ """
+ # Check if data/results folder exists, if not create it
+ Path(directory + '/results').mkdir(parents=True, exist_ok=True)
+
+ # Recover dictionary values
+ list_of_vars = []
+ for v in instance.component_objects(ctype=pyo.Var, active=True, descend_into=True):
+ for e in v._data:
+ v._data[e] = value(v[e])
+ list_of_vars.append(v)
+
+ # Open xlsx writer
+ writer = pd.ExcelWriter(directory + '/results/' + name, engine='xlsxwriter')
+
+ # Raw results
+ for v in list_of_vars:
+ try:
+ if str(v) == 'inv_flows' or str(v) == 'inv_vector':
+ data = [(k, itervention_map[k], v) for k, v in v._data.items()]
+ else:
+ data = [(k, process_map[k], v) for k, v in v._data.items()]
+ df = pd.DataFrame(data, columns=['ID', 'Process', 'Value'])
+ except:
+ data = [(k, v) for k, v in v._data.items()]
+ df = pd.DataFrame(data, columns=['Key', 'Value'])
+ df.sort_values(by=['Value'], inplace=True, ascending=False)
+ df.to_excel(writer, sheet_name=v.name, index=False)
+
+ # Normalize database to a list if it is a string
+ if isinstance(database, str):
+ database = [database]
+
+ # Store the metadata
+ metadata = [f"{project}__{db}" for db in database]
+ pd.DataFrame(metadata).to_excel(writer, sheet_name='project and db')
+
+ metadata = {}
+ for choice in choices:
+ i = 0
+ temp_dict = []
+ for alt in choices[choice]:
+ temp_dict.append((alt, i, instance.scaling_vector[process_map[alt.key]]))
+ i+=1
+ metadata[(choice, 'Process')] = {'Process ' + str(i): process_map[process_map[alt.key]] for alt, i, val in temp_dict}
+ metadata[(choice, 'Capacity')] = {'Process ' + str(i): choices[choice][alt] for alt, i, val in temp_dict}
+ metadata[(choice, 'Value')] = {'Process ' + str(i): x for alt, i, x in temp_dict}
+
+ pd.DataFrame(metadata).to_excel(writer, sheet_name='choices')
+
+ metadata = {}
+ metadata['Demand'] = {
+ process_map[process_map[key]] if key in process_map else key: demand[key]
+ for key in demand
+ }
+ pd.DataFrame(metadata).to_excel(writer, sheet_name='demand')
+
+ metadata = {}
+ metadata['Demand'] = {process_map[process_map[key]]: constraints[key] for key in constraints}
+ pd.DataFrame(metadata).to_excel(writer, sheet_name='constraints')
+
+ # Save xlsx file
+ writer.close()
+ return
+
+
+
+[docs]
+def summarize_results(instance, choices, constraints, demand, process_map, zeroes):
+ """
+ Summarizes the results of the optimization and prints them to the console.
+
+ Args:
+ instance: The Pyomo model instance.
+ choices (dict): Choices for the model.
+ constraints (dict): Constraints applied during optimization.
+ demand (dict): Demand data used in optimization.
+ process_map (dict): Mapping of process IDs to descriptions.
+ zeroes (bool): Whether to include zero values in the summary.
+ """
+ metadata = {}
+ metadata['Demand'] = {
+ process_map[process_map[key]] if key in process_map else key: demand[key]
+ for key in demand
+ }
+
+ print('The following demand / functional unit has been specified: ')
+ display(pd.DataFrame(metadata))
+
+ # Recover dictionary values
+ list_of_vars = []
+ for v in instance.component_objects(ctype=pyo.Var, active=True, descend_into=True):
+ for e in v._data:
+ v._data[e] = value(v[e])
+ list_of_vars.append(v)
+
+ # Raw results
+ for v in list_of_vars:
+ if v.name == 'impacts':
+ try:
+ data = [(k, process_map[k], v) for k, v in v._data.items()]
+ df = pd.DataFrame(data, columns=['ID', 'Process', 'Value'])
+ except:
+ data = [(k, v) for k, v in v._data.items()]
+ df = pd.DataFrame(data, columns=['Key', 'Value'])
+ df.sort_values(by=['Value'], inplace=True, ascending=False)
+ print('\nThese are the impacts contained in the objective:')
+ display(df)
+
+ if v.name == 'impacts_calculated':
+ try:
+ data = [(k, process_map[k], v) for k, v in v._data.items()]
+ df = pd.DataFrame(data, columns=['ID', 'Process', 'Value'])
+ except:
+ data = [(k, v) for k, v in v._data.items()]
+ df = pd.DataFrame(data, columns=['Key', 'Value'])
+ df.sort_values(by=['Value'], inplace=True, ascending=False)
+ print('\nThe following impacts were calculated: ')
+ display(df)
+
+ print('\nThe following choices were made: ')
+ for choice in choices:
+ metadata = {}
+ i = 0
+ temp_dict = []
+ for alt in choices[choice]:
+ if zeroes == False or instance.scaling_vector[process_map[alt.key]] != 0:
+ temp_dict.append((alt, i, instance.scaling_vector[process_map[alt.key]]))
+ i += 1
+ metadata[(choice, 'Process')] = {'Process ' + str(i): process_map[process_map[alt.key]] for alt, i, val in temp_dict}
+ metadata[(choice, 'Capacity')] = {'Process ' + str(i): choices[choice][alt] for alt, i, val in temp_dict}
+ metadata[(choice, 'Value')] = {'Process ' + str(i): x for alt, i, x in temp_dict}
+ print(choice)
+ display(pd.DataFrame(metadata))
+
+ if constraints == {}:
+ print('No additional constraints have been passed.')
+ else:
+ metadata = {}
+ metadata['Constraints'] = {process_map[process_map[key]]: constraints[key] for key in constraints}
+ print('\nThe following constraints were implemented and oblieged: ')
+ display(pd.DataFrame(metadata))
+
+
+
+
+
+
+
+import bw2data as bd
+import bw2calc as bc
+from packaging import version
+
+
+[docs]
+def is_bw25():
+ """Check if the installed Brightway packages adhere to bw25 versions."""
+ # Define version thresholds
+ THRESHOLDS = {
+ "bw2calc": "2.0.dev5",
+ "bw2data": "4.0.dev11",
+ }
+
+ try:
+ for pkg, threshold in {"bw2calc": bc, "bw2data": bd}.items():
+ pkg_version = ".".join(map(str, threshold.__version__)) if isinstance(threshold.__version__,
+ tuple) else str(
+ threshold.__version__)
+ if version.parse(pkg_version) < version.parse(THRESHOLDS[pkg]):
+ return False
+ return True
+ except Exception as e:
+ raise RuntimeError(f"Error checking Brightway versions: {e}")
+
+Short
+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js new file mode 100644 index 0000000..2ea7ff3 --- /dev/null +++ b/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = `` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = `` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js new file mode 100644 index 0000000..dbe1aaa --- /dev/null +++ b/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/docs/_static/custom.css b/_static/custom.css similarity index 100% rename from docs/_static/custom.css rename to _static/custom.css diff --git a/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css new file mode 100644 index 0000000..eb19f69 --- /dev/null +++ b/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}a.sd-bg-success:focus,a.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}.sd-bg-info{background-color:var(--sd-color-info) !important}.sd-bg-text-info{color:var(--sd-color-info-text) !important}button.sd-bg-info:focus,button.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}a.sd-bg-info:focus,a.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}.sd-bg-warning{background-color:var(--sd-color-warning) !important}.sd-bg-text-warning{color:var(--sd-color-warning-text) !important}button.sd-bg-warning:focus,button.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) 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window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..4d67807 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..f38abe9 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/docs/_static/favicon-100.png b/_static/favicon-100.png similarity index 100% rename from docs/_static/favicon-100.png rename to _static/favicon-100.png diff --git a/docs/_static/favicon-144.png b/_static/favicon-144.png similarity index 100% rename from docs/_static/favicon-144.png rename to _static/favicon-144.png diff --git a/docs/_static/favicon-500.png b/_static/favicon-500.png similarity index 100% rename from docs/_static/favicon-500.png rename to _static/favicon-500.png diff --git a/docs/_static/favicon.svg b/_static/favicon.svg similarity index 100% rename from docs/_static/favicon.svg rename to _static/favicon.svg diff --git a/docs/_static/favicon_mask-icon.svg b/_static/favicon_mask-icon.svg similarity index 100% rename from docs/_static/favicon_mask-icon.svg rename to _static/favicon_mask-icon.svg diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/_static/file.png differ diff --git a/_static/graphviz.css b/_static/graphviz.css new file mode 100644 index 0000000..027576e --- /dev/null +++ b/_static/graphviz.css @@ -0,0 +1,19 @@ +/* + * graphviz.css + * ~~~~~~~~~~~~ + * + * Sphinx stylesheet -- graphviz extension. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +img.graphviz { + border: 0; + max-width: 100%; +} + +object.graphviz { + max-width: 100%; +} diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 0000000..367b8ed --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file mode 100644 index 0000000..d96755f Binary files 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formatting so we can control at the div level */ +.cell_input div.highlight, +.cell_output pre, +.cell_input pre, +.cell_output .output { + border: none; + box-shadow: none; +} + +.cell_output .output pre, +.cell_input pre { + margin: 0px; +} + +/* Input cells */ +div.cell div.cell_input, +div.cell details.above-input>summary { + padding-left: 0em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + background-color: var(--mystnb-source-bg-color); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; + border-radius: var(--mystnb-source-border-radius); +} + +div.cell_input>div, +div.cell_output div.output>div.highlight { + margin: 0em !important; + border: none !important; +} + +/* All cell outputs */ +.cell_output { + padding-left: 1em; + padding-right: 0em; + margin-top: 1em; +} + +/* Text outputs from cells */ +.cell_output .output.text_plain, +.cell_output .output.traceback, +.cell_output 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border-bottom-right-radius: 0; + border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed; + padding-left: 1em; + margin-bottom: 0; +} + +div.cell details.above-output>summary { + background-color: var(--mystnb-source-bg-color); + padding-left: 1em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + border-radius: var(--mystnb-source-border-radius); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; +} + +div.cell details.below-input>summary { + background-color: var(--mystnb-source-bg-color); + padding-left: 1em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + border-top: none; + border-bottom-left-radius: var(--mystnb-source-border-radius); + border-bottom-right-radius: var(--mystnb-source-border-radius); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; +} + +div.cell details.hide>summary>span { + opacity: var(--mystnb-hide-prompt-opacity); +} + +div.cell details.hide[open]>summary>span.collapsed { + display: none; +} + +div.cell details.hide:not([open])>summary>span.expanded { + display: none; +} + +@keyframes collapsed-fade-in { + 0% { + opacity: 0; + } + + 100% { + opacity: 1; + } +} +div.cell details.hide[open]>summary~* { + -moz-animation: collapsed-fade-in 0.3s ease-in-out; + -webkit-animation: collapsed-fade-in 0.3s ease-in-out; + animation: collapsed-fade-in 0.3s ease-in-out; +} + +/* Math align to the left */ +.cell_output .MathJax_Display { + text-align: left !important; +} + +/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */ +div.cell_output table { + border: none; + border-collapse: collapse; + border-spacing: 0; + color: black; + font-size: 1em; + table-layout: fixed; +} + +div.cell_output thead { + border-bottom: 1px solid black; + vertical-align: bottom; +} + +div.cell_output tr, +div.cell_output th, +div.cell_output td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} + +div.cell_output th { + font-weight: bold; +} + +div.cell_output tbody tr:nth-child(odd) { + background: #f5f5f5; +} + +div.cell_output tbody tr:hover { + background: rgba(66, 165, 245, 0.2); +} + +/** source code line numbers **/ +span.linenos { + opacity: 0.5; +} + +/* Inline text from `paste` operation */ + +span.pasted-text { + font-weight: bold; +} + +span.pasted-inline img { + max-height: 2em; +} + +tbody span.pasted-inline img { + max-height: none; +} + +/* Font colors for translated ANSI escape sequences +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors +*/ +div.highlight .-Color-Bold { + font-weight: bold; +} + +div.highlight .-Color[class*=-Black] { + color: #3E424D +} + +div.highlight .-Color[class*=-Red] { + color: #E75C58 +} + +div.highlight .-Color[class*=-Green] { + color: #00A250 +} + +div.highlight .-Color[class*=-Yellow] { + color: #DDB62B +} + +div.highlight .-Color[class*=-Blue] { + color: #208FFB +} + +div.highlight .-Color[class*=-Magenta] { + color: #D160C4 +} + +div.highlight .-Color[class*=-Cyan] { + color: #60C6C8 +} + +div.highlight .-Color[class*=-White] { + color: #C5C1B4 +} + +div.highlight .-Color[class*=-BGBlack] { + background-color: #3E424D +} + +div.highlight .-Color[class*=-BGRed] { + background-color: #E75C58 +} + +div.highlight .-Color[class*=-BGGreen] { + background-color: #00A250 +} + +div.highlight .-Color[class*=-BGYellow] { + background-color: #DDB62B +} + +div.highlight .-Color[class*=-BGBlue] { + background-color: #208FFB +} + +div.highlight .-Color[class*=-BGMagenta] { + background-color: #D160C4 +} + +div.highlight .-Color[class*=-BGCyan] { + background-color: #60C6C8 +} + +div.highlight .-Color[class*=-BGWhite] { + background-color: #C5C1B4 +} + +/* Font colors for 8-bit ANSI */ + +div.highlight .-Color[class*=-C0] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC0] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C1] { + color: #800000 +} + +div.highlight .-Color[class*=-BGC1] { + background-color: #800000 +} + +div.highlight .-Color[class*=-C2] { + color: #008000 +} + +div.highlight .-Color[class*=-BGC2] { + background-color: #008000 +} + +div.highlight .-Color[class*=-C3] { + color: #808000 +} + +div.highlight .-Color[class*=-BGC3] { + background-color: #808000 +} + +div.highlight .-Color[class*=-C4] { + color: #000080 +} + +div.highlight .-Color[class*=-BGC4] { + background-color: #000080 +} + +div.highlight .-Color[class*=-C5] { + color: #800080 +} + +div.highlight .-Color[class*=-BGC5] { + background-color: #800080 +} + +div.highlight .-Color[class*=-C6] { + color: #008080 +} + +div.highlight .-Color[class*=-BGC6] { + background-color: #008080 +} + +div.highlight .-Color[class*=-C7] { + color: #C0C0C0 +} + +div.highlight .-Color[class*=-BGC7] { + background-color: #C0C0C0 +} + +div.highlight .-Color[class*=-C8] { + color: #808080 +} + +div.highlight .-Color[class*=-BGC8] { + background-color: #808080 +} + +div.highlight .-Color[class*=-C9] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC9] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C10] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC10] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C11] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC11] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C12] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC12] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C13] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC13] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C14] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC14] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C15] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC15] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C16] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC16] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C17] { + color: #00005F +} + +div.highlight .-Color[class*=-BGC17] { + background-color: #00005F +} + +div.highlight .-Color[class*=-C18] { + color: #000087 +} + +div.highlight .-Color[class*=-BGC18] { + background-color: #000087 +} + +div.highlight .-Color[class*=-C19] { + color: #0000AF +} + +div.highlight .-Color[class*=-BGC19] { + background-color: #0000AF +} + +div.highlight .-Color[class*=-C20] { + color: #0000D7 +} + +div.highlight .-Color[class*=-BGC20] { + background-color: #0000D7 +} + +div.highlight .-Color[class*=-C21] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC21] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C22] { + color: #005F00 +} + +div.highlight .-Color[class*=-BGC22] { + background-color: #005F00 +} + +div.highlight .-Color[class*=-C23] { + color: #005F5F +} + +div.highlight .-Color[class*=-BGC23] { + background-color: #005F5F +} + +div.highlight .-Color[class*=-C24] { + color: #005F87 +} + +div.highlight .-Color[class*=-BGC24] { + background-color: #005F87 +} + +div.highlight .-Color[class*=-C25] { + color: #005FAF +} + +div.highlight .-Color[class*=-BGC25] { + background-color: #005FAF +} + +div.highlight .-Color[class*=-C26] { + color: #005FD7 +} + +div.highlight .-Color[class*=-BGC26] { + background-color: #005FD7 +} + +div.highlight .-Color[class*=-C27] { + color: #005FFF +} + +div.highlight .-Color[class*=-BGC27] { + background-color: #005FFF +} + +div.highlight .-Color[class*=-C28] { + color: #008700 +} + +div.highlight .-Color[class*=-BGC28] { + background-color: #008700 +} + +div.highlight .-Color[class*=-C29] { + color: #00875F +} + +div.highlight .-Color[class*=-BGC29] { + background-color: #00875F +} + +div.highlight .-Color[class*=-C30] { + color: #008787 +} + +div.highlight .-Color[class*=-BGC30] { + background-color: #008787 +} + +div.highlight .-Color[class*=-C31] { + color: #0087AF +} + +div.highlight .-Color[class*=-BGC31] { + background-color: #0087AF +} + +div.highlight .-Color[class*=-C32] { + color: #0087D7 +} + +div.highlight .-Color[class*=-BGC32] { + background-color: #0087D7 +} + +div.highlight .-Color[class*=-C33] { + color: #0087FF +} + +div.highlight .-Color[class*=-BGC33] { + background-color: #0087FF +} + +div.highlight .-Color[class*=-C34] { + color: #00AF00 +} + +div.highlight .-Color[class*=-BGC34] { + background-color: #00AF00 +} + +div.highlight .-Color[class*=-C35] { + color: #00AF5F +} + +div.highlight .-Color[class*=-BGC35] { + background-color: #00AF5F +} + +div.highlight .-Color[class*=-C36] { + color: #00AF87 +} + +div.highlight .-Color[class*=-BGC36] { + background-color: #00AF87 +} + +div.highlight .-Color[class*=-C37] { + color: #00AFAF +} + +div.highlight .-Color[class*=-BGC37] { + background-color: #00AFAF +} + +div.highlight .-Color[class*=-C38] { + color: #00AFD7 +} + +div.highlight .-Color[class*=-BGC38] { + background-color: #00AFD7 +} + +div.highlight .-Color[class*=-C39] { + color: #00AFFF +} + +div.highlight .-Color[class*=-BGC39] { + background-color: #00AFFF +} + +div.highlight .-Color[class*=-C40] { + color: #00D700 +} + +div.highlight .-Color[class*=-BGC40] { + background-color: #00D700 +} + +div.highlight .-Color[class*=-C41] { + color: #00D75F +} + +div.highlight .-Color[class*=-BGC41] { + background-color: #00D75F +} + +div.highlight .-Color[class*=-C42] { + color: #00D787 +} + +div.highlight .-Color[class*=-BGC42] { + background-color: #00D787 +} + +div.highlight .-Color[class*=-C43] { + color: #00D7AF +} + +div.highlight .-Color[class*=-BGC43] { + background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + background-color: #AF875F +} + +div.highlight .-Color[class*=-C138] { + color: #AF8787 +} + +div.highlight .-Color[class*=-BGC138] { + background-color: #AF8787 +} + +div.highlight .-Color[class*=-C139] { + color: #AF87AF +} + +div.highlight .-Color[class*=-BGC139] { + background-color: #AF87AF +} + +div.highlight .-Color[class*=-C140] { + color: #AF87D7 +} + +div.highlight .-Color[class*=-BGC140] { + background-color: #AF87D7 +} + +div.highlight .-Color[class*=-C141] { + color: #AF87FF +} + +div.highlight .-Color[class*=-BGC141] { + background-color: #AF87FF +} + +div.highlight .-Color[class*=-C142] { + color: #AFAF00 +} + +div.highlight .-Color[class*=-BGC142] { + background-color: #AFAF00 +} + +div.highlight .-Color[class*=-C143] { + color: #AFAF5F +} + +div.highlight .-Color[class*=-BGC143] { + background-color: #AFAF5F +} + +div.highlight .-Color[class*=-C144] { + color: #AFAF87 +} + +div.highlight .-Color[class*=-BGC144] { + background-color: #AFAF87 +} + +div.highlight .-Color[class*=-C145] { + color: #AFAFAF +} + +div.highlight .-Color[class*=-BGC145] { + background-color: #AFAFAF +} + +div.highlight .-Color[class*=-C146] { + color: #AFAFD7 +} + +div.highlight .-Color[class*=-BGC146] { + background-color: #AFAFD7 +} + +div.highlight .-Color[class*=-C147] { + color: #AFAFFF +} + +div.highlight .-Color[class*=-BGC147] { + background-color: #AFAFFF +} + +div.highlight .-Color[class*=-C148] { + color: #AFD700 +} + +div.highlight .-Color[class*=-BGC148] { + background-color: #AFD700 +} + +div.highlight .-Color[class*=-C149] { + color: #AFD75F +} + +div.highlight .-Color[class*=-BGC149] { + background-color: #AFD75F +} + +div.highlight .-Color[class*=-C150] { + color: #AFD787 +} + +div.highlight .-Color[class*=-BGC150] { + background-color: #AFD787 +} + +div.highlight .-Color[class*=-C151] { + color: #AFD7AF +} + +div.highlight .-Color[class*=-BGC151] { + background-color: #AFD7AF +} + +div.highlight .-Color[class*=-C152] { + color: #AFD7D7 +} + +div.highlight .-Color[class*=-BGC152] { + background-color: #AFD7D7 +} + +div.highlight .-Color[class*=-C153] { + color: #AFD7FF +} + +div.highlight .-Color[class*=-BGC153] { + background-color: #AFD7FF +} + +div.highlight .-Color[class*=-C154] { + color: #AFFF00 +} + +div.highlight .-Color[class*=-BGC154] { + background-color: #AFFF00 +} + +div.highlight .-Color[class*=-C155] { + color: #AFFF5F +} + +div.highlight .-Color[class*=-BGC155] { + background-color: #AFFF5F +} + +div.highlight .-Color[class*=-C156] { + color: #AFFF87 +} + +div.highlight .-Color[class*=-BGC156] { + background-color: #AFFF87 +} + +div.highlight .-Color[class*=-C157] { + color: #AFFFAF +} + +div.highlight .-Color[class*=-BGC157] { + background-color: #AFFFAF +} + +div.highlight .-Color[class*=-C158] { + color: #AFFFD7 +} + +div.highlight .-Color[class*=-BGC158] { + background-color: #AFFFD7 +} + +div.highlight .-Color[class*=-C159] { + color: #AFFFFF +} + +div.highlight .-Color[class*=-BGC159] { + background-color: #AFFFFF +} + +div.highlight .-Color[class*=-C160] { + color: #D70000 +} + +div.highlight .-Color[class*=-BGC160] { + background-color: #D70000 +} + +div.highlight .-Color[class*=-C161] { + color: #D7005F +} + +div.highlight .-Color[class*=-BGC161] { + background-color: #D7005F +} + +div.highlight .-Color[class*=-C162] { + color: #D70087 +} + +div.highlight .-Color[class*=-BGC162] { + background-color: #D70087 +} + +div.highlight .-Color[class*=-C163] { + color: #D700AF +} + +div.highlight .-Color[class*=-BGC163] { + background-color: #D700AF +} + +div.highlight .-Color[class*=-C164] { + color: #D700D7 +} + +div.highlight .-Color[class*=-BGC164] { + background-color: #D700D7 +} + +div.highlight .-Color[class*=-C165] { + color: #D700FF +} + +div.highlight .-Color[class*=-BGC165] { + background-color: #D700FF +} + +div.highlight .-Color[class*=-C166] { + color: #D75F00 +} + +div.highlight .-Color[class*=-BGC166] { + background-color: #D75F00 +} + +div.highlight .-Color[class*=-C167] { + color: #D75F5F +} + +div.highlight .-Color[class*=-BGC167] { + background-color: #D75F5F +} + +div.highlight .-Color[class*=-C168] { + color: #D75F87 +} + +div.highlight .-Color[class*=-BGC168] { + background-color: #D75F87 +} + +div.highlight .-Color[class*=-C169] { + color: #D75FAF +} + +div.highlight .-Color[class*=-BGC169] { + background-color: #D75FAF +} + +div.highlight .-Color[class*=-C170] { + color: #D75FD7 +} + +div.highlight .-Color[class*=-BGC170] { + background-color: #D75FD7 +} + +div.highlight .-Color[class*=-C171] { + color: #D75FFF +} + +div.highlight .-Color[class*=-BGC171] { + background-color: #D75FFF +} + +div.highlight .-Color[class*=-C172] { + color: #D78700 +} + +div.highlight .-Color[class*=-BGC172] { + background-color: #D78700 +} + +div.highlight .-Color[class*=-C173] { + color: #D7875F +} + +div.highlight .-Color[class*=-BGC173] { + background-color: #D7875F +} + +div.highlight .-Color[class*=-C174] { + color: #D78787 +} + +div.highlight .-Color[class*=-BGC174] { + background-color: #D78787 +} + +div.highlight .-Color[class*=-C175] { + color: #D787AF +} + +div.highlight .-Color[class*=-BGC175] { + background-color: #D787AF +} + +div.highlight .-Color[class*=-C176] { + color: #D787D7 +} + +div.highlight .-Color[class*=-BGC176] { + background-color: #D787D7 +} + +div.highlight .-Color[class*=-C177] { + color: #D787FF +} + +div.highlight .-Color[class*=-BGC177] { + background-color: #D787FF +} + +div.highlight .-Color[class*=-C178] { + color: #D7AF00 +} + +div.highlight .-Color[class*=-BGC178] { + background-color: #D7AF00 +} + +div.highlight .-Color[class*=-C179] { + color: #D7AF5F +} + +div.highlight .-Color[class*=-BGC179] { + background-color: #D7AF5F +} + +div.highlight .-Color[class*=-C180] { + color: #D7AF87 +} + +div.highlight .-Color[class*=-BGC180] { + background-color: #D7AF87 +} + +div.highlight .-Color[class*=-C181] { + color: #D7AFAF +} + +div.highlight .-Color[class*=-BGC181] { + background-color: #D7AFAF +} + +div.highlight .-Color[class*=-C182] { + color: #D7AFD7 +} + +div.highlight .-Color[class*=-BGC182] { + background-color: #D7AFD7 +} + +div.highlight .-Color[class*=-C183] { + color: #D7AFFF +} + +div.highlight .-Color[class*=-BGC183] { + background-color: #D7AFFF +} + +div.highlight .-Color[class*=-C184] { + color: #D7D700 +} + +div.highlight .-Color[class*=-BGC184] { + background-color: #D7D700 +} + +div.highlight .-Color[class*=-C185] { + color: #D7D75F +} + +div.highlight .-Color[class*=-BGC185] { + background-color: #D7D75F +} + +div.highlight .-Color[class*=-C186] { + color: #D7D787 +} + +div.highlight .-Color[class*=-BGC186] { + background-color: #D7D787 +} + +div.highlight .-Color[class*=-C187] { + color: #D7D7AF +} + +div.highlight .-Color[class*=-BGC187] { + background-color: #D7D7AF +} + +div.highlight .-Color[class*=-C188] { + color: #D7D7D7 +} + +div.highlight .-Color[class*=-BGC188] { + background-color: #D7D7D7 +} + +div.highlight .-Color[class*=-C189] { + color: #D7D7FF +} + +div.highlight .-Color[class*=-BGC189] { + background-color: #D7D7FF +} + +div.highlight .-Color[class*=-C190] { + color: #D7FF00 +} + +div.highlight .-Color[class*=-BGC190] { + background-color: #D7FF00 +} + +div.highlight .-Color[class*=-C191] { + color: #D7FF5F +} + +div.highlight .-Color[class*=-BGC191] { + background-color: #D7FF5F +} + +div.highlight .-Color[class*=-C192] { + color: #D7FF87 +} + +div.highlight .-Color[class*=-BGC192] { + background-color: #D7FF87 +} + +div.highlight .-Color[class*=-C193] { + color: #D7FFAF +} + +div.highlight .-Color[class*=-BGC193] { + background-color: #D7FFAF +} + +div.highlight .-Color[class*=-C194] { + color: #D7FFD7 +} + +div.highlight .-Color[class*=-BGC194] { + background-color: #D7FFD7 +} + +div.highlight .-Color[class*=-C195] { + color: #D7FFFF +} + +div.highlight .-Color[class*=-BGC195] { + background-color: #D7FFFF +} + +div.highlight .-Color[class*=-C196] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC196] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C197] { + color: #FF005F +} + +div.highlight .-Color[class*=-BGC197] { + background-color: #FF005F +} + +div.highlight .-Color[class*=-C198] { + color: #FF0087 +} + +div.highlight .-Color[class*=-BGC198] { + background-color: #FF0087 +} + +div.highlight .-Color[class*=-C199] { + color: #FF00AF +} + +div.highlight .-Color[class*=-BGC199] { + background-color: #FF00AF +} + +div.highlight .-Color[class*=-C200] { + color: #FF00D7 +} + +div.highlight .-Color[class*=-BGC200] { + background-color: #FF00D7 +} + +div.highlight .-Color[class*=-C201] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC201] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C202] { + color: #FF5F00 +} + +div.highlight .-Color[class*=-BGC202] { + background-color: #FF5F00 +} + +div.highlight .-Color[class*=-C203] { + color: #FF5F5F +} + +div.highlight .-Color[class*=-BGC203] { + background-color: #FF5F5F +} + +div.highlight .-Color[class*=-C204] { + color: #FF5F87 +} + +div.highlight .-Color[class*=-BGC204] { + background-color: #FF5F87 +} + +div.highlight .-Color[class*=-C205] { + color: #FF5FAF +} + +div.highlight .-Color[class*=-BGC205] { + background-color: #FF5FAF +} + +div.highlight .-Color[class*=-C206] { + color: #FF5FD7 +} + +div.highlight .-Color[class*=-BGC206] { + background-color: #FF5FD7 +} + +div.highlight .-Color[class*=-C207] { + color: #FF5FFF +} + +div.highlight .-Color[class*=-BGC207] { + background-color: #FF5FFF +} + +div.highlight .-Color[class*=-C208] { + color: #FF8700 +} + +div.highlight .-Color[class*=-BGC208] { + background-color: #FF8700 +} + +div.highlight .-Color[class*=-C209] { + color: #FF875F +} + +div.highlight .-Color[class*=-BGC209] { + background-color: #FF875F +} + +div.highlight .-Color[class*=-C210] { + color: #FF8787 +} + +div.highlight .-Color[class*=-BGC210] { + background-color: #FF8787 +} + +div.highlight .-Color[class*=-C211] { + color: #FF87AF +} + +div.highlight .-Color[class*=-BGC211] { + background-color: #FF87AF +} + +div.highlight .-Color[class*=-C212] { + color: #FF87D7 +} + +div.highlight .-Color[class*=-BGC212] { + background-color: #FF87D7 +} + +div.highlight .-Color[class*=-C213] { + color: #FF87FF +} + +div.highlight .-Color[class*=-BGC213] { + background-color: #FF87FF +} + +div.highlight .-Color[class*=-C214] { + color: #FFAF00 +} + +div.highlight .-Color[class*=-BGC214] { + background-color: #FFAF00 +} + +div.highlight .-Color[class*=-C215] { + color: #FFAF5F +} + +div.highlight .-Color[class*=-BGC215] { + background-color: #FFAF5F +} + +div.highlight .-Color[class*=-C216] { + color: #FFAF87 +} + +div.highlight .-Color[class*=-BGC216] { + background-color: #FFAF87 +} + +div.highlight .-Color[class*=-C217] { + color: #FFAFAF +} + +div.highlight .-Color[class*=-BGC217] { + background-color: #FFAFAF +} + +div.highlight .-Color[class*=-C218] { + color: #FFAFD7 +} + +div.highlight .-Color[class*=-BGC218] { + background-color: #FFAFD7 +} + +div.highlight .-Color[class*=-C219] { + color: #FFAFFF +} + +div.highlight .-Color[class*=-BGC219] { + background-color: #FFAFFF +} + +div.highlight .-Color[class*=-C220] { + color: #FFD700 +} + +div.highlight .-Color[class*=-BGC220] { + background-color: #FFD700 +} + +div.highlight .-Color[class*=-C221] { + color: #FFD75F +} + +div.highlight .-Color[class*=-BGC221] { + background-color: #FFD75F +} + +div.highlight .-Color[class*=-C222] { + color: #FFD787 +} + +div.highlight .-Color[class*=-BGC222] { + background-color: #FFD787 +} + +div.highlight .-Color[class*=-C223] { + color: #FFD7AF +} + +div.highlight .-Color[class*=-BGC223] { + background-color: #FFD7AF +} + +div.highlight .-Color[class*=-C224] { + color: #FFD7D7 +} + +div.highlight .-Color[class*=-BGC224] { + background-color: #FFD7D7 +} + +div.highlight .-Color[class*=-C225] { + color: #FFD7FF +} + +div.highlight .-Color[class*=-BGC225] { + background-color: #FFD7FF +} + +div.highlight .-Color[class*=-C226] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC226] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C227] { + color: #FFFF5F +} + +div.highlight .-Color[class*=-BGC227] { + background-color: #FFFF5F +} + +div.highlight .-Color[class*=-C228] { + color: #FFFF87 +} + +div.highlight .-Color[class*=-BGC228] { + background-color: #FFFF87 +} + +div.highlight .-Color[class*=-C229] { + color: #FFFFAF +} + +div.highlight .-Color[class*=-BGC229] { + background-color: #FFFFAF +} + +div.highlight .-Color[class*=-C230] { + color: #FFFFD7 +} + +div.highlight .-Color[class*=-BGC230] { + background-color: #FFFFD7 +} + +div.highlight .-Color[class*=-C231] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC231] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C232] { + color: #080808 +} + +div.highlight .-Color[class*=-BGC232] { + background-color: #080808 +} + +div.highlight .-Color[class*=-C233] { + color: #121212 +} + +div.highlight .-Color[class*=-BGC233] { + background-color: #121212 +} + +div.highlight .-Color[class*=-C234] { + color: #1C1C1C +} + +div.highlight .-Color[class*=-BGC234] { + background-color: #1C1C1C +} + +div.highlight .-Color[class*=-C235] { + color: #262626 +} + +div.highlight .-Color[class*=-BGC235] { + background-color: #262626 +} + +div.highlight .-Color[class*=-C236] { + color: #303030 +} + +div.highlight .-Color[class*=-BGC236] { + background-color: #303030 +} + +div.highlight .-Color[class*=-C237] { + color: #3A3A3A +} + +div.highlight .-Color[class*=-BGC237] { + background-color: #3A3A3A +} + +div.highlight .-Color[class*=-C238] { + color: #444444 +} + +div.highlight .-Color[class*=-BGC238] { + background-color: #444444 +} + +div.highlight .-Color[class*=-C239] { + color: #4E4E4E +} + +div.highlight .-Color[class*=-BGC239] { + background-color: #4E4E4E +} + +div.highlight .-Color[class*=-C240] { + color: #585858 +} + 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