Metadata-Version: 2.1
Name: treads
Version: 1.0.1
Summary: Tool for Recovery Estimation And Downtime Simulation of buildings
Home-page: https://github.com/carlosmolinahutt/treads
Author: Pouria Kourehpaz
Author-email: pouria.kourehpaz@ubc.ca
License: UNKNOWN
Description: <p align="center">
        <img src="https://github.com/carlosmolinahutt/ESR-Lab-Repositories/blob/master/dt_framework.png" 
        	height="350"/>
        </p>
        
        <h1 align = "center"> TREADS: Tool for Recovery Estimation And Downtime Simulation of buildings</h1>
        	    
        </p>
        
        ## Introduction
        
        `treads` is a Python package to evaluate earthquake induced downtime and model recovery of buildings. This tool implements the framework presented in: 
        
        Molina Hutt, C., Vahanvaty, T. and Kourehpaz, P. (2021). “An analytical framework to assess earthquake induced downtime and model recovery of buildings.” *Earthquake Spectra*, Accepted.
        
        This tool is fully compatible with SimCenter’s tool for loss assessment, i.e., pelicun (https://github.com/NHERI-SimCenter/pelicun) 
        
        ## Requirements
        
        `treads` runs under Python 3.6+. The following packages are required for it to work properly:
        
        `numpy`  `pandas` `os` `sys` `more_itertools` `json` 
        
        You can install these using `pip`.
        
        ## Installation
        
        `treads` is available at the Python Package Index (PyPI). You can simply install it using `pip` as follows:
        
        ```
        pip install treads
        ```
        ## Basic Demo
        ```python
        import DT_calculation 	# refer to "Example" folder
        
        input_parameters = 'input_parameters.json'
        RCtable_input = 'Repair_Class_Table.csv'
        IF_delays_input = 'IF_delays_input.csv'
        
        DMG_input = 'DMG.csv' 	# pelicun output
        DL_summary_input = 'DL_summary.csv' 	# pelicun output
        DV_rec_time_input = 'DV_rec_time.csv' 	# pelicun output
        
        output_path = '**insert output directory here**'
        
        DT_calculation.run_treads(input_parameters, RCtable_input, IF_delays_input, DMG_input, DL_summary_input, DV_rec_time_input, output_path)
        ```
        ## Outputs
        
        `treads` estimates earthquake-induced downtime to achieve Functional Recovery (FR), Re-Occupancy (RO), and Shelter-in-Place (SiP) post-earthquake recovery states for residential buildings. The following output files will be generated once you run `treads`: 
        
        - **RC_component.csv:**  Component repair class matrix. 
        - **DT_summary.csv:**  10th percentile, 90th percentile, median, and mean downtime estimates.
        - **RS_stats.csv:**  Probability of a building not achieving different recovery states immediately after an earthquake. 
        - **DT_stepfunc_xx.csv:**  Governing recovery trajectories to each recovery state (xx= FR, RO, SiP).
        - **DT_path_xx.xlsx:**  Recovery trajectories to each recovery state for each repair path (xx= FR, RO, SiP).
        - **RT_stepfunc_xx.xlsx:**  Repair time stepping functions for each repair sequence when each recovery state is achieved (xx= FR, RO, SiP).
        - **RT_RSeq_xx.csv:**  Repair time per story for each repair sequence when each recovery state is achieved (xx= FR, RO, SiP).
        - **IF_delays.csv:**  Impeding factor delays. 
        
        
        ## Tutorials
        YouTube tutorials coming soon.
        
        ## Contact
        
        Pouria Kourehpaz, University of British Columbia, Vancouver, BC, Canada. email: pouria.kourehpaz@ubc.ca
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
