Portfolio recovery

Description

The code creates two output files building-recovery.csv and portfolio-recovery.csv

Input Parameters

key name	type	name	description
result_name	str	Result name	Name of the result dataset.
uncertainty *	bool	Uncertainty	Additional randomness.
sample_size	int	Sample size	Number of buildings to be considered from buildings dataset.
random_sample_size *	int	Sample size	Number of iterations for the Monte Carlo simulation.
no_of_weeks *	int	Number weeks	Number of weeks to run the recovery model.
num_cpu	int	Number of CPUs	Number of CPUs used for parallel computations. Default 1.

Input Datasets

key name	type	name	description
building_data *	incore:portfolioBuildingInventory	Building dataset	A building dataset.
occupancy_mapping *	incore:portfolioOccupancyMapping	Occupancy mapping	An occupancy of buildings dataset.
building_damage *	incore:portfolioBuildingDamage	Building damage	A building damage.
dmg_ratios *	incore:portfolioDamageRatios	Damage ratios	Mean repair by occupancy and building type.
utility *	incore:portfolioUtilityAvailability	Utility availability	Utility availability at utility service area.
utility_partial *	incore:portfolioUtilityAvailability	Utility availability	Partial utility availability at utility service area.
coefFL *	incore:portfolioCoefficients	Initial coefficients	Correlation coefficient of initial functionality.

Output Datasets

key name	type	name	description
result *	incore:portfolioRecovery	Results	A dataset containing results (format: CSV).

(* required)

Execution

code snippet:

    # Create instance
    bldg_portfolio_recovery = BuildingPortfolioRecoveryAnalysis(client)

    # Load input datasets
    bldg_portfolio_recovery.load_remote_input_dataset("building_data", bldg_data_dataset)
    bldg_portfolio_recovery.load_remote_input_dataset("occupancy_mapping", occupancy_dataset)
    bldg_portfolio_recovery.load_remote_input_dataset("building_damage", bldg_damage_dataset)
    bldg_portfolio_recovery.load_remote_input_dataset("dmg_ratios", mean_repair_dataset)
    bldg_portfolio_recovery.load_remote_input_dataset("utility", utility_dataset)
    bldg_portfolio_recovery.load_remote_input_dataset("utility_partial", utility_partial_dataset)
    bldg_portfolio_recovery.load_remote_input_dataset("coefFL", coefFL_dataset)

    # Set parameters
    bldg_portfolio_recovery.set_parameter("uncertainty", True)
    bldg_portfolio_recovery.set_parameter("sample_size", 35)  # default none. Gets size form input dataset
    bldg_portfolio_recovery.set_parameter("random_sample_size", 50)  # default 10000
    bldg_portfolio_recovery.set_parameter("no_of_weeks", 100)  # default 250

    # Creates two output files building-recovery.csv and portfolio-recovery.csv
    bldg_portfolio_recovery.run_analysis()

full analysis: portfolio_recovery.ipynb